Methods and systems for investigation of compositions of ontological subjects and intelligent systems therefrom

ABSTRACT

Methods and systems are given for investigation of compositions of ontological subjects in accordance with various aspects of significance. Accordingly, the present invention provide a unified method and process of investigating the compositions of ontological subjects, modeling an unknown system, and obtaining as much worthwhile information and knowledge as possible about the system or the composition or the body of knowledge along with exemplary services utilizing such investigations. The data structures built and the knowledge acquired by a machine through executing the investigation methods of the present disclosure enables artificial intelligent systems, machines, and agents to perform intelligent tasks and jobs.

CROSS-REFERENCED TO RELATED APPLICATIONS

This application is a continuation in part of and claims the benefits ofthe U.S. patent application Ser. No. 14/694,887 entitled “Methods andSystems For investigation Of Compositions of Ontological Subjects” filedon Apr. 23, 2015 which claims the priority and the benefits from theU.S. patent application Ser. No. 13/608,333 entitled “Methods andSystems For investigation Of Compositions of Ontological Subjects” filedon Sep. 10, 2012, which claims priority to U.S. provisional patentapplication No. 61/546,054 filed on Oct. 10, 2011 entitled the same, andwhich also cross-referenced and claimed the benefits of:

the U.S. patent application Ser. No. 12/179,363 entitled “ASSISTEDKNOWLEDGE DISCOVERY AND PUBLICATION SYSTEM AND METHOD”, filed on Jul.24-2008, which claims priority from Canadian Patent Application Ser. NoCA 2,595,541, filed on Jul. 26, 2007, entitled the same; and

the U.S. patent application Ser. No. 13/789,644 filed on Mar. 7, 2013which is a continuation of U.S. patent application Ser. No. U/547,879filed on Aug. 26, 2009, now U.S. Pat. No. 8,452,725, entitled “SYSTEMAND METHOD OF ONTOLOGICAL SUBJECT MAPPING FOR KNOWLEDGE PROCESSINGAPPLICATIONS”, which claims priority from the U.S. provisional patentapplication No. 61/093,952 filed on Sep. 3 2008, entitled the same; and

the U.S. patent application Ser. No. 14,151,022 filed on Jan. 9, 2014which is a continuation in part of the U.S. patent application Ser. No.13/962,895, now U.S. Pat. No. 8,983,897, filed on Aug. 8, 2013, entitled“UNIFIED SEMANTIC RANKING OF COMPOSITIONS OF ONTOLOGICAL SUBJECTS” whichis a divisional of and claims the benefit of the U.S. patent applicationSer. No. 12/755,415, now U.S. Pat. No. 8,612,445, filed on Apr. 7, 2010,which claims priority from U.S. provisional patent application No.61/177,696 filed on May 13, 2009 entitled: “System and Method for aUnified Semantic Ranking of Compositions of Ontological Subjects and theApplications Thereof”; and

the U.S. patent application Ser. No. 12/908,856 entitled “SYSTEM ANDMETHOD OF CONTENT GENERATION”, filed on Oct. 20, 2010, which claimspriority from U.S. provisional application No. 61/253,511 filed on Oct.21, 2009, entitled the same; and

the U.S. patent application Ser. No. 14/607,588, filed on Feb. 7, 215,entitled “Association strengths and value significances of ontologicalsubjects of networks and compositions” which is a divisional of andclaims the benefits of the U.S. patent application Ser. No. 13/740,228,filed on Jan. 13, 2013, entitled “SYSTEM AND METHOD FOR VALUESIGNIFICANCE EVALUATION OF ONTOLOGICAL SUBJECTS OF NETWORKS AND THEAPPLICATION THEREOF” which is a divisional of and claims the benefits ofthe U.S. patent application Ser. No. 12/939,112, filed on Nov. 3, 2010,now U.S. Pat. No. 8,401,980, entitled “METHODS FOR DETERMINING CONTEXTOF COMPOSITIONS OF ONTOLOGICAL SUBJECTS AND THE APPLICATIONS THEREOFUSING VALUE SIGNIFICANCE MEASURES (VSMS), CO-OCCURRENCES AND FREQUENCYOF OCCURRENCES OF THE ONTOLOGICAL SUBJECTS SYSTEM”, which claimspriority from U.S. provisional application No. 61/259,640 filed on Nov.10, 2009, entitled “SYSTEM AND METHOD FOR VALUE SIGNIFICANCE EVALUATIONOF ONTOLOGICAL SUBJECTS OF NETWORKS AND THE APPLICATION THEREOF”; and

the U.S. patent application Ser. No. 14/018,102, filed on Sep. 4, 2014,which is a divisional of and claims the benefits of the U.S. patentapplication Ser. No. 12/946,838, filed on Nov. 15, 2010, now U.S. Pat.No. 8,560,599 B2 entitled: “AUTOMATIC CONTENT COMPOSITION GENERATION”,which claims priority from U.S. provisional application No. 61/263,685filed on Nov. 23, 2009, entitled “Automatic Content CompositionGeneration”; and

the U.S. patent application Ser. No. 14,247,731, filed on May 11, 214which is a continuation of the U.S. patent application Ser. No.12/955,496, filed on Nov. 29, 2010, now U.S. Pat. No. 8,775,365 entitled“INTERACTIVE AND SOCIAL KNOWLEDGE DISCOVERY SESSIONS” which claimspriority from U.S. provisional patent application No. 61/311,368 filedon Mar. 7, 2010, entitled “Interactive and Social Knowledge DiscoverySessions”, all by the same applicant which are all incorporated entirelyas references in this application.”; and

The U.S. application Ser. No. 14/616,687 entitled “Ontological SubjectsOf A Universe And Knowledge Processing Thereof” filed on Feb. 7, 2015.

This application also cross-references and claims the benefits of theco-pending U.S. application Ser. No. 15/589,914 entitled “OntologicalSubjects Of A Universe And Knowledge Representations Thereof” filed onMay 8, 2017, which is also incorporated here by reference along with thereferenced applications therein in their entirety for all purposes.

FIELD OF INVENTION

This invention generally relates to information processing, ontologicalsubject processing, knowledge processing and discovery, computationalgenomics, knowledge retrieval, artificial intelligence, signalprocessing, information theory, natural language processing and theapplications.

BACKGROUND OF THE INVENTION

In these day and age that data is generated at an unprecedented rate itis very hard for a human operator to analyze large bodies of data inorder to extract the real information, the knowledge therein, spot anovelty, and using them to further advance the state of knowledge ordiscovery of a real knowledge about a subject matter.

For example for any topic or subject there are vast amount of textual,or convertible to textual characters, repositories such as collection ofresearch papers in any particular topic or subject, images, news feeds,interviews, talks, video collections, corporate databases, surveillancepictures and videos, and the like. Gaining any benefit from suchunstructured collections of information needs lots of expertise, time,and many years of training just even to separate the facts and extractvalue out of these immense amounts of data. Not every piece of data isworthy of attention and investigation or investment of expensive timesof experts and professionals or data processing resources.

Moreover, there is no guarantee that a human investigator or researchercan accurately analyze the vast collection of documents, data, andinformation. The results of the investigations are usually biased by theindividual's knowledge, experiences, and background. The complexities ofrelations in the bodies of data limit the throughputs of knowledge-basedprofessionals and the speed at which credible knowledge can be produced.The desired speed or rate of knowledge discovery apparently is muchhigher than the present rate of knowledge discovery and production.

SUMMARY OF THE INVENTION

There is a need to enhance the art of knowledge discovery andinvestigation methods in terms of accuracy, effectiveness on unknowncompositions, thoroughness, speed, and throughput.

Additionally, in some instances, there could be compositions such as, analien language composition, a body of knowledge unfamiliar to anindividual investigator, a corporate database, a computer code program,a collection of reports, genetic code strings and the like that we donot have any prior information about the meaning and implications ofthese compositions and the parts therein. Investigating suchcompositions is of immense interest and value.

It is also very desirable to enable a data processing system, such as acomputer system comprise of data processing or computing devices/units,data storage units/devices, and/or environmental data acquisitionsunits/devices, and/or data communication units/devices, and/orinput/output units/devices, and/or limbs, to learn as much informationand gain knowledge/data by processing compositions of data of variousforms and/or become able to produce new knowledge and useful data orcompositions of data and/or autonomous decision making according to somecodes of conducts. Such an enabled machine would be of an immenseassistance to the development of human civilization much further andmuch faster leading to abundance, economic prosperity, biological andmental health, and well-being of society in general.

Accordingly, the present invention discloses a systematic, computerimplementable, process efficient and scalable method/s of investigationof all types of compositions of ontological subjects such as textual,data files, networks and graphs, genetic codes, any types of string, andthe likes. The given methods, algorithms, and services are accompaniedwith theoretical modeling and mathematical formulations which, onceimplemented, results in robust and fundamental algorithms and processesfor investigating various aspects of a composition and for numerousapplications.

According to the teachings of the present invention any compositions ofontological subjects is viewed as an unknown system or system ofknowledge that the purpose of the investigation is to obtain as muchworthy information and knowledge about such an unknown system.

The present invention therefore investigate the “compositions ofontological subjects” or a “body of knowledge” or a “system ofknowledge” (as are called from time to time in this disclosure) byproviding the investigation methods for identifying the most significantconstituent ontological subjects for a given body of knowledge or thegiven compositions in respect to one or more significance aspect/s. Thesignificance aspects generally include the “intrinsic significanceaspects” and/or “associational/relational significance aspects”.

In the general aspect of this invention, conceptual “measures ofsignificances” are disclosed along with their rational andjustifications. These conceptual “measures of significances” further areaccompanied with systematic methods of calculation and quantificationsof their values in order to provide the instrumental tools inimplementations/utilization of the disclosed method/s of theinvestigation of compositions of ontological subjects. These measuresare, for example, called “value significance measures” (VSM/s in short),“association strength measures” (or ASM for short), “novelty valuesignificance measures” (or NVSM for short), and/or“relational/associational” type measures, and various combinations ofthem (referred herein as XY_VSM in general form) that are used to findand spot the “aspectual significant” parts or partitions of thecomposition for further investigation and/or further processing and/orpresentation to a client.

According to one general embodiment of the disclosed method/s of thepresent invention, a composition of ontological subjects or a body ofknowledge is break down to it's constituent ontological subjects whichare grouped in different set which each set labeled with differentorders, from which one or more array of data, respective of theinformation of the participations of the constituent ontologicalsubjects of different orders into each other, are formed. The datatherefore is used to evaluate various significance values of theconstituent ontological subjects of the different order according to thedisclosed measures of various aspects of significance.

Accordingly, in one aspect of the present invention, measure/s are givenfor valuation of “value significances” of the ontological subjects ofthe composition. These values are intrinsic values of the ontologicalsubjects of the composition based on their significance role which iscalculated from the participations pattern/s of the ontological subjectsof the composition with each other.

In another aspect various measures of “association strength” are givenfrom which the relations of ontological subjects of the composition canbe revealed. Algorithms and formulations and calculation methods aregiven to evaluate such “association strength” according to variousexemplary association aspects.

According to another aspect of the present invention measures are givenfor evaluating the “relational association strengths” of the ontologicalsubjects of different orders to each other or to one or more targetontological subject.

According to another aspect of the present invention measures are givenfor evaluating the “relational value significances” of the ontologicalsubjects of different orders to each other or to one or more targetontological subject.

According to another aspect of the invention, various types of measuresare given to evaluate the “novelty value significances” of theontological subjects of the composition or the body of knowledge.Method/s are, therefore, given for efficient calculations and processingand presentation of the results.

Accordingly, in yet another aspect of the invention, various measure ofthe “relational novelty value significances” are given for evaluatingone type of the general “novelty value significances” in relation to oneor more target ontological subjects of the composition or the body ofknowledge.

According to yet another aspect of the invention various measure of the“associational novelty value significances” are given for evaluatinganother type of the general “novelty value significance” involving theassociation of one or more target ontological subjects of thecomposition or the body of knowledge.

According to yet another aspect of the invention various measure of the“intrinsic novelty value significances” are given for evaluating yetanother type of “novel value significance” which is an intrinsic noveltyvalue of one or more of ontological subjects of the composition or thebody of knowledge.

According to another aspect of the invention, the values are assigned toa predetermined list of ontological subjects (e.g. one or more of thespecial words that usually are used to express a particular attributesuch as a novelty or a reasoning or concluding remarks, such as‘therefore, consequently, in spite of, . . . however, but, . . . etc.).These are called “special significance conveyers” to pre-selectedlyamplify or dampen the significances of such special OSs of a compositionin eth final output or result.

Furthermore, specific examples and general forms and methods are givenas how to synthesize and/or shape a desired from of a “valuesignificance measure” and how to build and calculate the respectivefilter for that “value significance measure” by combining one or more ofthe USM vectors of one or more type or number of the XY-VSM.

These various “XY-value significance measures” then can be employed inmany applications for which at least one “aspectual significancemeasure” is of interest and importance. Depends on the desiredapplication one can use the applicable and desirable embodiments for theintended application such as web page ranking, document clustering,single and multi-document summarization/distillation, questionanswering, graphical representation of the compositions, contextextraction and representation, knowledge discovery, novelty detection,composing new compositions, engineering new compositions, compositioncomparison, approximate reasoning, artificial intelligence, robotic,robotics vision, human/computer interaction, computer conversation, aswell as other areas of science and technology such as genetic analysisand synthesize, signal processing, economics, marketing, customer care,and the like.

Along the disclosure, methods, formulations, and algorithms are givenfor efficient and versatile computer implementable evaluation of thevarious “value significance measures” of ontological subject ofdifferent orders used in a system of knowledge. In essence, using theparticipation information of a set of lower order OSs into a set of thesame or higher order OSs, the present invention provide a unified methodand process of investigating the compositions of ontological subjects,modeling an unknown system, and obtaining as much worthwhile informationand knowledge as possible about the system or the composition or thebody of knowledge. The “aspectual investigation's goals” can bewide-open, however, in light of the teachings of the present inventionbecomes a straightforward, implementable, and practical possibility.

Accordingly, in another aspect of the invention, a number of exemplaryapplications are described and presented with the illustrating blockdiagrams of the method and algorithm along with the associated systemsfor performing such applications. These applications and systems arepresented to exemplify the way that the present invention's methods ofinvestigations might be employed to perform one or more of the desiredprocesses to get the respective output or the content, answer, data,graphs, analysis, etc.

Therefore beside that an ontological subjects of a composition is notonly represented by a string of characters but also there would beadditional vast information available for the ontological subjectcorresponding to its type/s of significance and relationship with otherontological subjects of the composition. Said additional information ordata is learnt, through implementing the methods of current disclosureand the incorporated references herein, from the ways these ontologicalsubjects being used or composed together to make up a composition ormore generally to form a body of knowledge.

These information, data, or values of different objects of thisdisclosure (e.g. association strength measures, significance measureetc.) are placed in one or more data structures which can berepresentative of data arrays corresponding to vectors or matrix forconvenience of calculations by data processing devices. The dataprocessing devices to carry out the calculations, storing, and datatransportation between the various part of one or more computer systemscan be selected from such technologies such as electronic or opticalbased processors, semiconductor based or quantum computers, applicationspecific processing devices and the like. Different embodiments aregiven for ease of calculations and processing the data of said one ormore data structures or vectors or matrices than can be implement withinformation, computing, or data processing systems of certain processingspeeds and/or storage media access speed and capacities such as certainRAM capacity, SSD, HD, and/or optical memories and the like withrequired access time.

In this way the implicit information not recognizable, useable, orappreciable by a human (due to inherent biological limitations) can beextracted, stored and become useable by a data processing system ormachine. Said data processing system or machine therefore will becomeable to use its superior processing speed and unmatched, by human,memory capacity or environmental data acquisition capabilities, toperform intelligent tasks. Examples of such intelligent tasks could be,but of course not limited to, conversing intelligently or evaluating amerit of a composition, recognizing visual objects, DNA analysis,knowledge discovery, automatic research and discovery, or composing anessay or a multimedia content, decision making, automatic knowledgediscovery, controlling physical action/reaction of a machine of itslimbs, management of tasks and sessions, autonomous navigation, and ingeneral such tasks that currently can only be done by human being.Intelligent beings (or artificially intelligent beings) of variouskinds, technologies, and forms, (e.g. a humanoid robot maid, agenetically modified being, a transportation intelligent beings such aan autonomous car or an autonomous agricultural machine, a roboticexplorer, etc.), are exemplary beneficiaries of implementing andemploying the methods and systems of the current disclosure.

More illustrating application system examples are further given toillustrate the application of the methods and systems of this disclosurein implementing neural networks more efficiently and processing imagesfor such applications such as image recognition and environmentalknowledge acquisition through visual data files.

All the methods, the systems and applications of this disclosure areused to implement a real and useful intelligent being which is capableperforming intelligent tasks, such as recognition of objects, conversingwith human client/user/master, reasoning, new knowledge discovery,navigation instructions, and all types of intelligent assistant systems,either by imbedding the software and computable/executable codes into adesired system or by specific physical building of such systems.

According to another aspect of the present invention, there are providedembodiments for using the investigation methods of compositions andbodies of knowledge to build and initialize a machine learning neuralnetworks and training such networks. Further embodiments and exemplarymethods and systems are given for using the methods of this disclosurein image and visual content processing.

Further, in another aspect, the invention provides data processingsystems comprising computer hardware, software, internet infrastructure,and other customary appliances of an E-business, cloud computing,distributed networks, and services to perform and execute said methodsin providing a variety of services for a client/user's desiredapplications or to provide a needed or requested data to a human/agentclient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: shows one exemplary block diagram of a system or a softwareartifact that generates various outputs from a body of knowledge or acomposition according to one embodiment of the present invention.

FIG. 2: shows one exemplary illustration of the concept of associationstrength of a pair of OSs according to one embodiment of the presentinvention.

FIG. 3: shows one exemplary embodiment of a directed asymmetric networkor graph corresponding to a composition of ontological subjects.

FIG. 4: shows a block diagram of one preferred embodiment of the methodand the algorithm for calculating a number of exemplary “ValueSignificance Measures” of different types for the ontological subjectsof a composition according to one embodiment of the present invention.

FIG. 5: shows one exemplary block diagram of the method and thealgorithm of building the “Ontological Subject Maps” (OSM) from the“Association Strength Matrix” (ASM) which is built for and from an inputcomposition according to one embodiment of the present invention.

FIGS. 6a, 6b, 6c , show the exemplary values and one way of representingthe values of the different conveyers of the different types of the“value significance measures”.

FIG. 7: shows one exemplary instance of implementing the formulationsand algorithm/s illustrating one way of using the “participation matrix”(PM) and the “association strength matrix” (ASM) to calculate the twodifferent types of the associations strength of the OSs of order 2 tothe OSs of the order l, according to one embodiment of the presentinvention. This Figure is to demonstrate the use of various VSM vectors(filters) in the calculations.

FIG. 8: is an block diagram the system and method of building at leasttwo participation matrixes and calculating VSM for lth order partition,OS^(l), to calculate the “Value Significance Measures” (VSM) of otherpartitions of the compositions, OS^(l+r) and storing them for furtheruse by the application servers according to one embodiment of thepresent invention.

FIG. 9: a block diagram of an exemplary application and the associatedsystem for ranking, filtering, storing, indexing, clustering the crawledwebpages, from the internet or other repositories, using “ValueSignificance Measures” (VSM) according to one embodiment of the presentinvention.

FIG. 10 is an exemplary system of investigating module/s forinvestigation of composition of ontological subjects providing one ormore desired result/data/output according to one embodiment of thepresent invention.

FIG. 11-A: shows an exemplary application and realization of thedisclosed method using a neural network in which the connection weightbetween neurons is adjusted using the various associations strengthsmeasure according to the teaching of this disclosure a block diagram ofan exemplary application for investigation of a body of news feeds.

FIG. 11-B: illustrate an exemplary application and realization of thedisclosed method in investigation visual compositions such asimages/movie frames/pictures composed of data of corresponding to theconstituent pixels. One exemplary choice of partitioning an image isgiven. Dark or white rectangles are indicatives of a pixels. All theinvestigation methods of this disclosure are therefore can be used toinvestigate and process an image or set of images (e.g. a video clip).

FIG. 11-C: is a block diagram of an exemplary application forinvestigation of a body of news feeds.

FIG. 12: is another exemplary general system of using the investigatorproviding various services to the clients over a communication network(e.g. a private or public) according to one embodiment of the presentinvention. This embodiment shows exemplary general architecture of asystem in which one or more of the blocks are optional and can beomitted or one or more blocks can be added.

FIG. 13: is another exemplary block diagram of a compositioninvestigation service for a client request for service according to oneembodiment of the present invention. One or more functional modules canbe still added to this embodiment and/or one or more of the modules canbe removed or disabled.

FIG. 14: An exemplary system of using the investigator providing variousservices to the clients in a private or public cloud environmentaccording to one embodiment of the present invention.

FIG. 15: another exemplary block diagram of a system of providing thevarious ubiquities service to one or more clients over a network whereinthe system can be either localized or distributed according to oneembodiment of the present invention.

DETAILED DESCRIPTION I—Intruduction

A system of knowledge, here, means a composition or a body of knowledgein any field, narrow or wide, composed of data symbols such asalphabetical/numerical characters, any array of data, binary orotherwise, or any string of data etc. In this disclosure, however, forthe sake and ease of explanation and comprehension, we mostly exemplifythe compositions and bodies of knowledge with those that are expressedin natural language symbols with textual characters

Accordingly, for instance a system of knowledge can be defined about theprocess of stem cell differentiation. In this example there are manyunknowns that are desired to be known. So consider someone has collectedmany or all textual compositions about this subject. Apparently thecollections contains many useful information about the subject that areimportant but can easily be overlooked by a human due to the limitationsof processing capability and memory capacity of individuals' brains.

Another example of a body of knowledge according to the givendefinitions is a picture or a video signal. A picture or a video frameis consists of colored pixels that have participated in a picture toform and convey the information about the picture. Apparently somecolored pixels of the picture are more significant or play a moredistinguishing role in that picture. Moreover their combination or theway or the pattern that they participate together in any small parts orsegments of that picture are also important in the way the pixels areconveying the information about the picture to an observer's eyes or acamera.

Yet another example of a composition or a body of knowledge could be astring of genetic codes, a DNA string, or a DNA strand, a whole genome,and the like.

Moreover any system, simple or complicated, can be identified andexplained by its constituent parts and the relation between the parts.Additionally, any system or body of knowledge can also be represented bynetwork/s or graphs that shows the connection and relations of theindividual parts of the system. The more accurate and detailed theidentification of the parts and their relations the better the system isdefined and designed and ultimately the better the correspondingtangible systems will function. Most of the information about any typeof existing or new systems can be found in the body of many textualcompositions. Nevertheless, these vast bodies of knowledge areunstructured, dispersed, and unclear for non expert in the field.

In the present invention, the purpose of the investigation is to modeland gain as much information and knowledge about an unknown systemcomprised of ontological subjects while the source of the informationabout such a system is a given composition of ontological subjectswherein the composition is readable by a computer. Therefore, someinformation about such an unknown system is supposedly embedded in abody of knowledge or system of knowledge or generally in the givencomposition. The investigator, hence, will have to be able to capture orproduce as much knowledge about the system from the information in thegiven composition.

Consequently, according to the present disclosure, the investigation isperformed according to at least one significant/important aspect in theinvestigation of bodies of knowledge (i.e. compositions).

The “investigation important aspect” can, for example, be one or more ofthe following goals:

1. identifying and recognizing the most significant constitutes parts ofthe bodies of knowledge according to at least one “significance aspect”,

2. identifying the associated constituent parts of the bodies ofknowledge, and

3. identifying and/or finding (through discovery and/or reasoning) theinformative constituent parts and informative combinations of theconstituent parts of the composition by, for example, finding orcomposing the expressions that show a relationship between two or moreof constituent parts of the bodies of knowledge.

Each of these “important aspect” or stages (1, 2, and 3 in the above) ofthe investigation, of course, can further be break down to two or morestages or steps or be combined together to perform a desirableinvestigation goal or to define the “investigation important aspect”.

For instance, according to one exemplary investigation method embodimentof the present invention, the “investigation important aspect” is toidentify a relationship between two or more significant parts of thecomposition, the investigator may perform the following:

-   -   1. identifying the most significant constituent part/s,    -   2. identifying the associated constituent parts of the bodies of        knowledge, and    -   3. finding or composing expressions that express the        relationship between one or more significant parts having        certain level of association to one or more of other significant        parts.

Therefore depends on the goal of the investigation the “investigationimportant aspect” can be defined and performed in more detailedprocesses. The present invention gives a number of such investigationgoals and the methods of achieving the desired outcome. Moreover, thepresent invention provides a variety of tools and investigation methodsthat enables a user to deal with investigation of compositions ofontological subjects for any kind of goals and any types of thecomposition.

As defined along this disclosure as well as the incorporated referencesherein, the constituent parts of the bodies of knowledge are called“Ontological Subjects” (OS). The ontological subjects further aregrouped into different sets labeled with orders as will be explained inthe definition of section of this disclosure too.

The “significance aspects”, based on which the significances of the OSsof compositions are defined and calculated, are various that can belooked at. For instance one “significance aspect” could be an intrinsicsignificance of an OS which shows the overall or intrinsic significanceof an OS in a body of knowledge. Another significance aspect isconsidered to be a significant aspect in relation or relative to one ormore of the OSs of the body of knowledge.

Yet another significance aspect is considered to be an intrinsic noveltyvalue of an OS in a body of knowledge or a composition. And yet anothersignificance aspect is defined as a relative or relational novelty valueof an OS related to one or more of the OSs of the body of knowledge or acomposition.

Many other desirable significance aspect might be defined by differentpeople depends on the application and the goal of the investigation of acomposition or a body of knowledge. Also any combinations of suchsignificance aspects can be regarded as a significance aspect.

Accordingly a “significance aspect” is the orientation that one can useto reason on how to put a significance value on an ontological subjectof a composition or a body of knowledge.

In other words, a “significance aspect” is a qualitative quality thatcan polarize or differentiate the ontological subjects and be used todefine “value significance measures” and consequently suggest orconstruct various value functions or significance weighting functions onthe ontological subjects of a composition or a body of knowledge.

These functions, individually or in combination, therefore can beemployed and utilized to spot and/or filter out the one or moreontological subjects of a composition or a body of knowledge fordifferent purposes and applications or generally for investigation ofbodies of knowledge.

For instance and in accordance with one aspect of the presentdisclosure, for the purpose of investigation of the compositions ofontological subjects, a general form of evaluating “value significances”of the ontological subjects of a composition or a body of knowledge or anetwork is given along with a number of exemplified such valuesignificances and their applications. Such investigation method/s willspeed up the research process and knowledge discovery, and design cyclesby guiding the users to know the substantiality of each part in thesystem. Consequently dealing with all parts of the system based on thevalue significance priority or any other predetermined criteria canbecome a systematic process and more yielding to automation.

As will be explained in the next section, having constructed one or morearrays of data indicative of relations of constituent part, it willbecome necessary and desirable to spot the significant part and/orseparate the parts that their significance is defined in relation to atarget part. Thereby relational value significances are defined here.The relational value significances are instrumental in clustering acollection of composition or clustering partitions of composition inregards to one or more of a target OS or the parts of the system ofknowledge.

Furthermore exemplary algorithms and systems are given to be used forproviding the respective data and/or such application/s as one or moreservices to the computer program agents as well as human users.

Application of such methods and systems of investigations ofcompositions of ontological subjects would be very many and various. Forexample lets say after or before a conference, with many expertparticipants and many presented papers, one wants to compare thesubmitted contributing papers, draw some conclusions, and/or get thedirection for future research or find the more important subjects tofocus on, he or she could use the system, employing the disclosedmethods, to find out the value significance of each concept along withtheir most important associations and interrelations. This is not aneasy task for the individuals who do not have many years of experienceand a deep and wide breadth of knowledge in the respective domain ofknowledge.

Or consider a market research analyst who is assigned to find out thereal value of an enterprise by researching the various sources ofinformation. Or rank an enterprise among its competitors by identifyingthe strength and weakness of the enterprise constituent parts orpartitions. Or in another instance an enterprise, a blogger, a websiteowner, a content publisher, or a Facebook subscriber wants to find outthe most valuable or the most interesting contents, comments, or anyparts of such discussions. The investigation method of the presentinvention therefore can provide such information and knowledge with highconfidence.

Many other consecutive applications such as searching engines, questionanswering, summarization, categorization, distillation, computerconversing, artificial intelligence, genetics, etc. can be performed,enhanced, and benefit from having an estimation of the various “valuesignificances” of the partitions of the body of knowledge and a throughinvestigation method of such compositions.

In order to describe the disclosure in details we first define a numberof terms that are used frequently throughout this description. Forinstance, the information bearing symbols are called OntologicalSubjects and are defined herein below, along with others terms, in thedefinitions sections.

I-I—Definitions

This disclosure uses the definitions that were introduced in the U.S.patent application Ser. No. 12/755,415 filed on Apr. 7, 2010, and Ser.No. 12/939,112 filed on Nov. 3, 2010, which are incorporated herein asreferences, and are recited here again along with more clarifying pointsaccording to their usage in this disclosure and the mathematicalformulations herein.

1. ONTOLOGICAL SUBJECT: symbol or signal referring to a thing (tangibleor otherwise) worthy of knowing about. Therefore Ontological Subjectmeans generally any string of characters, but more specifically,characters, letters, numbers, words, binary codes, bits, mathematicalfunctions, sound signal tracks, video signal tracks, electrical signals,chemical molecules such as DNAs and their parts, or any combinations ofthem, and more specifically all such string combinations that indicatesor refer to an entity, concept, quantity, and the incidences of suchentities, concepts, and quantities. In this disclosure OntologicalSubject/s and the abbreviation OS or OSs are used interchangeably.

2. ORDERED ONTOLOGICAL SUBJECTS: Ontological Subjects can be dividedinto sets with different orders depends on their length, attribute, andfunction. Basically the order is assigned to a group or set ofontological subjects having at least one common predefined attribute,property, attribute, or characteristic. Usually the orders in thisdisclosure are denoted with alpha numerical characters such as 0, 1, 2,etc or OS1, OS2, etc. or any other combination of characters so as todistinguish one group or set of ontological subjects, having at leastone common predefined characteristic, with another set or group ofontological subjects having another at least one common characteristic.This order/s will also be reflected in denoting/corresponding the dataobjects or the mathematical objects in the formulations to distinguishthese data objects in relation to their corresponding ontologicalsubject set or its order, as will be used and introduced throughout thisdisclosure. For instance, for ontological subjects of textual nature,one may characterizes or label letters as zeroth order OS, words ormultiple word phrases as the first order, sentences or multiple wordphrases as the second order, paragraphs as the third order, pages orchapters as the fourth order, documents as the fifth order, corpuses asthe sixth order OS and so on. As seen the order can be assigned to agroup or set of ontological subjects based on at least one commonpredefined characteristic of the members of the set. So a higher orderOS is a combination of, or a set of, lower order OSs or lower order OSsare members of a higher order OS. Equally one can order the geneticcodes in different orders of ontological subjects. For instance, the 4basis of a DNA molecules as the zeroth order OS, the base pairs as thefirst order, sets of pieces of DNA as the second order, genes as thethird order, chromosomes as the fourth order, genomes as the fifthorder, sets of similar genomes as the sixth order, sets of sets ofgenomes as the seventh order and so on. Yet the same can be defined forinformation bearing signals such as analogue and digital signalsrepresenting audio or video information. For instance for digitalsignals representing a signal, bits (electrical One and Zero) can bedefined as zeroth order OS, the bytes as first order, any sets of bytesas third order, and sets of sets of bytes, e.g. a frame, as fourth orderOS and so on. Yet in another instance for a picture or a video frame,the pixels with different color can be regarded as first order OS (theRGB values of a pixel can be regarded as zeroth order Oss), a set whosemembers contain two or more number of pixels (e.g. a segment of apicture) can be regarded as OSs of second order, a set whose memberscontain of two or more such segments as third order OS, a set whosemembers contain of two or more such third order OSs as fourth order OS,whole frame as fifth order OS, and a number of frames (like a certainperiod of duration of a movie such as a clip) as sixth order and so on.Therefore definitions of orders for ontological subjects are arbitraryset of initial definitions that one can stick to in order to make senseof the methods and mathematical formulations presented herein and beingable to interpret the consequent results or outcomes in more sensibleand familiar language.”

More importantly Ontological Subjects can be stored, processed,manipulated, and transported by transferring, transforming, and usingmatter or energy (equivalent to matter) and hence the OS processing isan instance of physical transformation of materials and energy.

3. COMPOSITION: is an OS composed of constituent ontological subjects oflower or the same order, particularly text documents written in naturallanguage documents, genetic codes, encryption codes, data files, voicefiles, video files, and any mixture thereof. A collection, or a set, ofcompositions is also a composition. Therefore a composition is in factan Ontological Subject of particular order which can be broken to lowerorder constituent Ontological Subjects. In this disclosure, thepreferred exemplary composition is a set of data containing ontologicalsubjects, for example a webpage, papers, documents, books, a set ofwebpages, sets of PDF articles, multimedia files, or even simply wordsand phrases. Moreover, compositions and bodies of knowledge arebasically the same and are used interchangeably in this disclosure.Compositions are distinctly defined here for assisting the descriptionin more familiar language than a technical language using only thedefined OSs notations.

4. PARTITIONS OF COMPOSITION: a partition of a composition, in general,is a part or whole, i.e. a subset, of a composition or collection ofcompositions. Therefore, a partition is also an Ontological Subjecthaving the same or lower order than the composition as an OS. Morespecifically in the case of textual compositions, parts or partitions ofa composition can be chosen to be characters, words, sentences,paragraphs, chapters, webpage, documents, etc. A partition of acomposition is also any string of symbols representing any form ofinformation bearing signals such as audio or videos, texts, DNAmolecules, genetic letters, genes, and any combinations thereof. Howeverone preferred exemplary definition of a partition of a composition inthis disclosure is word, sentence, paragraph, page, chapters, documents,sets of documents, and the like, or WebPages, and partitions of acollection of compositions can moreover include one or more of theindividual compositions. Partitions are also distinctly defined here forassisting the description in more familiar language than a technicallanguage using only the general OSs definitions.

5. SIGNIFICANCE MEASURE: assigning a quantity, or a number or feature ora metric for an OS from a set of OSs so as to assist to distinguishingor selecting one or more of the OSs from the set. More conveniently andin most cases the significance measure is a type of numerical quantityassigned to a partition of a composition. Therefore significancemeasures are functions of OSs and one or more of other relatedmathematical objects, wherein a mathematical object can, for instance,be a mathematical object containing information of participations of OSsin each other, whose values are used in the decisions about theconstituent OSs of a composition. For instance, “Relational, and/orassociational, and/or novel significances” are one form or a type of thegeneral “significance measures” concept and are defined according to oneor more the aspect of interest and/or in relation to one or more OSs ofthe composition.

6. FILTRATION/SUMMARIZATION: is a process of selecting one or more OSfrom one or more sets of OSs according to predetermined criteria with orwithout the help of value significance and ranking metric/s. Theselection or filtering of one or more OS from a set of OSs is usuallydone for the purposes of representation of a body of data by a summaryas an indicative of that body in respect to one or more aspect ofinterest. Specifically, therefore, in this disclosure searching througha set of partitions or compositions, and showing the search resultsaccording to the predetermined criteria is considered a form offiltration/summarization. In this view finding an answer to a query,e.g. question answering, or finding a composition related or similar toan input composition etc. is also a form of searching through a set ofpartitions and therefore are a form of summarization or filtrationaccording to the given definitions here.

7. THE USAGE OF QUOTATION MARKS “ ”: throughout the disclosure severalcompound names of concepts, variable, functions and mathematical objectsand their abbreviations (such as “participation matrix”, or PM forshort, “Co-Occurrence Matrix”, or COM for short, “value significancemeasure”, or VSM for short, and the like) will be introduced, either insingular or plural forms, that once or more is being placed between thequotation marks (“ ”) for identifying them as one object (or a regularexpression that is used in this disclosure frequently) and must not beinterpreted as being a direct quote from the literatures outside thisdisclosure.”

8. UNIVERSES OF COMPOSITIONS: Universe: in this disclosure “universe” isfrequently used and have few intended interpretation: when “universe x”(x is a number or letter or word or combination thereof) is used it meanthe universe of one or more compositions, that is called x, and containsnone, one or more ontological subjects. By “real universe” or “ouruniverse” we mean our real life universe including everything in it(physical and its notions and/or so called abstract and its notions)which is the largest universe intended and exist. Furthermore,“universal” refers to the real universe.

Furthermore, in the following description, numerous specific details areset forth in order to provide a thorough understanding of the presentembodiments. It will be apparent, however, to one having ordinary skillin the art that the specific detail need not be employed to practice thepresent embodiments. In other instances, well-known materials or methodshave not been described in detail in order to avoid obscuring thepresent embodiments.

-   1. Reference throughout this specification to “one embodiment”, “an    embodiment”, “one example” or “an example” means that a particular    feature, structure or characteristic described in connection with    the embodiment or example is included in at least one embodiment of    the present embodiments. Thus, appearances of the phrases “in one    embodiment”, “in an embodiment”, “for instance”, “one example” or    “an example” in various places throughout this specification are not    necessarily all referring to the same embodiment or example.    Furthermore, the particular features, structures or characteristics    may be combined in any suitable combinations and/or sub-combinations    in one or more embodiments or examples. In addition, it is    appreciated that the figures provided herewith are for explanation    purposes to persons ordinarily skilled in the art and that the    drawings are not necessarily drawn to scale.-   2. Embodiments in accordance with the present embodiments may be    implemented as an apparatus, method, or computer program product.    Accordingly, the present embodiments may take the form of an    entirely hardware embodiment, an entirely software embodiment    (including firmware, resident software, micro-code, etc.), or an    embodiment combining software and hardware aspects that may all    generally be referred to herein as a “module” or “system.”    Furthermore, the present embodiments may take the form of a computer    program product embodied in any tangible medium of expression having    computer-usable program code embodied in the medium.-   3. Any combination of one or more computer-usable or    computer-readable media may be utilized. For example, a    computer-readable medium may include one or more of a portable    computer diskette, a hard disk, a random access memory (RAM) device,    a read-only memory (ROM) device, an erasable programmable read-only    memory (EPROM or Flash memory) device, a portable compact disc    read-only memory (CDROM), an optical storage device, and a magnetic    storage device. Computer program code for carrying out operations of    the present embodiments may be written in any combination of one or    more programming languages.-   4. Embodiments may also be implemented in cloud computing    environments. In this description and the following claims, “cloud    computing” may be defined as a model for enabling ubiquitous,    convenient, on-demand network access to a shared pool of    configurable computing resources (e.g., networks, servers, storage,    applications, and services) that can be rapidly provisioned via    virtualization and released with minimal management effort or    service provider interaction, and then scaled accordingly. A cloud    model can be composed of various characteristics (e.g., on-demand    self-service, broad network access, resource pooling, rapid    elasticity, measured service, etc.), service models (e.g., Software    as a Service (“SaaS”), Platform as a Service (“PaaS”),    Infrastructure as a Service (“IaaS”), and deployment models (e.g.,    private cloud, community cloud, public cloud, hybrid cloud, etc.).-   5. The flowchart and block diagrams in the flow diagrams illustrate    the architecture, functionality, and operation of possible    implementations of systems, methods, and computer program products    according to various embodiments of the present embodiments. In this    regard, each block in the flowchart or block diagrams may represent    a module, segment, or portion of code, which comprises one or more    executable instructions for implementing the specified logical    function(s). It will also be noted that each block of the block    diagrams and/or flowchart illustrations, and combinations of blocks    in the block diagrams and/or flowchart illustrations, may be    implemented by special purpose hardware-based systems that perform    the specified functions or acts, or combinations of special purpose    hardware and computer instructions. These computer program    instructions may also be stored in a computer-readable medium that    can direct a computer or other programmable data processing    apparatus to function in a particular manner, such that the    instructions stored in the computer-readable medium produce an    article of manufacture including instruction means which implement    the function/act specified in the flowchart and/or block diagram    block or blocks.-   6. As used herein, the terms “comprises,” “comprising,” “includes,”    “including,” “has,” “having,” or any other variation thereof, are    intended to cover a non-exclusive inclusion. For example, a process,    article, or apparatus that comprises a list of elements is not    necessarily limited to only those elements but may include other    elements not expressly listed or inherent to such process, article,    or apparatus.-   7. Further, unless expressly stated to the contrary, “or” refers to    an inclusive or and not to an exclusive or. For example, a condition    A or B is satisfied by any one of the following: A is true (or    present) and B is false (or not present), A is false (or not    present) and B is true (or present), and both A and B are true (or    present).-   8. Additionally, any examples or illustrations given herein are not    to be regarded in any way as restrictions on, limits to, or express    definitions of any term or terms with which they are utilized.    Instead, these examples or illustrations are to be regarded as being    described with respect to one particular embodiment and as being    illustrative only. Those of ordinary skill in the art will    appreciate that any term or terms with which these examples or    illustrations are utilized will encompass other embodiments which    may or may not be given therewith or elsewhere in the specification    and all such embodiments are intended to be included within the    scope of that term or terms. Language designating such nonlimiting    examples and illustrations includes, but is not limited to: “for    example,” “for instance,” “e.g.,” and “in one embodiment.”

Now the invention is disclosed in details in reference to theaccompanying Figures and exemplary cases and embodiments in thefollowing subsections.

II—Description

The methods and systems that are devised here is to solve the proposedproblem of investigating compositions of ontological subjects throughalgorithmic manipulating and assigning and calculating various “valuesignificance” quantities to the constituent ontological subjects of acomposition or a network of ontological subjects. It is further todisclose the methods of measuring the significance of the value/s sothat the right “Value Significance Measure/s (USM)”, can be defined,synthesized, and be calculated for a desired aspect of investigation andbe used for further processing of many related applications or othermeasures.

The methods and systems of the present invention and can be used forapplications ranging from document classification, search enginedocument retrieval, news analysis, knowledge discovery and researchtrajectory optimization, question answering, computer conversation,spell checking, summarization, categorizations, categorization,clustering, distillation, automatic composition generation, genetics andgenomics, signal and image processing, to novel applications ineconomical systems by evaluating a value for economical entities, crimeinvestigation, financial applications such as financial decision making,credit checking, decision support systems, stock valuation, targetadvertising, and as well measuring the influence of a member in a socialnetwork, and/or any other problem that can be represented by graphs andfor any group of entities with some kind of relations or association.

Although the methods are general with broad applications, implications,and implementation strategies and technique, the disclosure is describedby way of specific exemplary embodiments to consequently describe themethods, implications, and applications in the simplest forms ofembodiments and senses.

Also since most of human knowledge and daily information production isrecorded in the form of text (or it can be converted or represented withtextual/numerical characters) the detailed description is focused ontextual compositions to illustrate the teachings and the methods and thesystems. In what follows the invention is described in several sectionsand steps which in light of the previous definitions would be sufficientfor those ordinary skilled in the art to comprehend and implement themethods, the systems and the applications thereof. In the followingsection we first set the mathematical foundation of the disclosed methodfrom where we launch into introducing several “value significancemeasures” (VSMs) and ways of calculating them and their applications.

We explain the method/s and the algorithms with the step by stepformulations that is easy to implement by those of ordinary skilled inthe art and by employing computer programming languages and computerhardware systems that can be optimized or customized by build or designof hardware to perform the algorithm efficiently and produce usefuloutputs for various desired applications.

II-I Participation Matrix Building for a Composition

Assuming we have an input composition of ontological subjects, e.g. aninput text, the “Participation Matrix” (PM) is a matrix indicating theparticipation of one or more ontological subjects of particular order inone or more partitions of the composition. In other words in terms ofour definitions, PM indicate the participation of one or more lowerorder OS into one or more OS of higher or the same order. PM/s are themost important array of data in this disclosure that contains the rawinformation from which many other important functions, information,features, and desirable parameters can be extracted. Without intendingany limitation on the value of PM entries, in the exemplary embodimentsthroughout most of this disclosure (unless stated otherwise) the PM is abinary matrix having entries of one or zero and is built for acomposition or a set of compositions as the following:

1. break the composition to desired numbers of partitions. For example,for a text document, break the documents into chapters, pages,paragraphs, lines, and/or sentences, words etc. and assign an ordernumber (e.g. 0, 1, 2, 3 . . . etc) to any set of similar partitions,i.e. the ordered ontological subjects,

2. select a desired N number of OSs of order k and a desired M number ofOSs of order l (these OSs are usually the partitions of the compositionfrom the step 1) according to certain predetermined criteria, and;

3. construct a N×M matrix in which the ith raw (R_(i)) is a vector (e.g.a binary vector), with dimension M, indicating the presence of the ithOS of order k, (often extracted from the composition underinvestigation), in the OSs of order l, (often extracted from thecomposition under investigation or sometimes from another referencedcomposition), by having a nonzero value, and not present by having thevalue of zero.

We call this matrix the “Participation Matrix” (usually a binary matrix)of the order kl (PM^(kl)) which can be represented as:

$\begin{matrix}{{PM}^{kl} = {\begin{matrix}{OS}_{1}^{k} \\\vdots \\{OS}_{N}^{k}\end{matrix}\overset{\begin{matrix}{OS}_{1}^{l} & \ldots & {OS}_{M}^{l}\end{matrix}}{\begin{pmatrix}{pm}_{11}^{kl} & \ldots & {pm}_{1\; M}^{kl} \\\vdots & \ddots & \vdots \\{pm}_{N\; 1}^{kl} & \ldots & {pm}_{NM}^{kl}\end{pmatrix}}}} & (1)\end{matrix}$

where OS_(p) ^(k) is the pth OS of the kth order (p=1 . . . N), OS_(q)^(l) is the qth OS of the lth order (q=1 . . . M), usually extractedfrom the composition, and, according to one embodiment of thisinvention, PM_(pq) ^(kl)=1 if OS_(p) ^(k) have participated, i.e. is amember, in the OS_(q) ^(l) and 0 otherwise. The desired criteria, in thestep 2 above, can be, for instance, to only select the content words orselect certain partitions having certain length or, in another instance,selecting all and every word or character strings and/or all thepartitions.

The participating matrix of order lk, i.e. PM^(lk), can also be definedwhich is simply the transpose of PM^(kl) whose elements are given by:

PM_(pq) ^(lk)=PM_(qp) ^(kl)  (2).

Accordingly without limiting the scope of invention, the description isgiven by exemplary embodiments using the general participation matrix ofthe order kl, i.e the PM^(kl) in which k≦l.

Furthermore PM carries much other useful information. For example usingbinary PMs, one can obtain a participation matrix in which the entriesare the number of time that a particular OS (e.g. a word) is beingrepeated in another partitions of particular interest (e.g. in adocument) one can readily do so by, for instance, the following:

PM_R ¹⁵=PM¹²×PM²⁵  (3)

wherein the PM_R¹⁵ stands for participation matrix of OSs of order 1(e.g. words) into OSs of order 5 (e.g. the documents) in which thenonzero entries shows the number of time that a word has been appearedin that document (however the possible repetition of a word in an OS oforder 2, e.g sentences, will not be accounted for here). Anotherapplicable example is using PM data to obtain the “frequency ofoccurrences” of ontological subjects in a given composition by:

FO_(i) ^(k|l)=Σ_(j)pm_(ij) ^(kl)  (4)

wherein the FO_(i) ^(k|l) is the frequency of occurrence of OSs of orderk, i.e. OS_(i) ^(k), in the OSs of order 1, i.e. the OS^(l). The lattertwo examples are given to demonstrate on how one can conveniently usethe PM and the disclosed method/s to obtain many other desired data orinformation.

More importantly, from PM^(kl) one can arrive at the “Co-OccurrenceMatrix” COM^(k|l) for OSs of the same order as follow:

COM^(k|l)=PM^(kl)*(PM^(kl))^(T)  (5),

where the “T” and “*” show the matrix transposition and multiplicationoperation respectively. The COM is a N×N square matrix. This is theco-occurrences of the ontological subjects of order k in the partitions(ontological subjects of order l) within the composition and is oneindication of the association of OSs of order k evaluated from theirpattern of participations in the OSs of order l of the composition. Theco-occurrence number is shown by com_(ij) ^(k|l) which is an element ofthe “Co-Occurrence Matrix (COM)” and (in the case of binary PMs)essentially showing that how many times OS_(i) ^(k) and OS_(j) ^(k) hasparticipated jointly into the selected OSs of the order l of thecomposition. Furthermore, COM can also be made binary, if desired, inwhich case only shows the existence or non-existence of a co-occurrencebetween any two OS^(k).

The importance of the “co-occurrence matrix” as defined in thisdisclosure is that it carries or contain the information of relationshipand associations of the OSs of the composition which is further utilizedin some embodiments of the present invention.

It should be noticed that the co-occurrences of ontological subjects canalso be obtained by looking at, for instance, co-occurrences of a pairof ontological subject within certain (i.e. predefined) proximities inthe composition (e.g. counting the number of times that a pair ofontological subjects have co-occurred within certain or predefineddistances from each other in the composition) as was used in theincorporated reference the U.S. patent application Ser. No. 12/179,363.Similarly there are other ways to count the frequency of occurrences ofan ontological subjects (i.e. the FO_(i) ^(k|l)). However the preferredembodiment is an efficient way of calculating these quantities orobjects and should not be construed as the only way implementing theteachings of the present invention. The repeated co-occurrences of apair of ontological subjects within certain proximities is an indicationof some sort of association (e.g. a logical relationship) between thepair or else it would have made no sense to use them together in one ormore partitions of the composition.

Those skilled in the art can store the information of the PMs, and alsoother mathematical objects of the present invention, in equivalent formswithout using the notion of a matrix. For example each raw of the PM canbe stored in a dictionary, or the PM be stored in a list or lists inlist, or a hash table, or a SQL database, or any other convenientobjects of any computer programming languages such as Python, C, Perl,Java, etc. Such practical implementation strategies can be devised byvarious people in different ways. Moreover, in the preferred exemplaryembodiments the PM entries are binary for ease of manipulation andcomputational efficiency.

However, in some applications it might be desired to have non-binaryentries so that to account for partial participation of lower orderontological subjects into higher orders, or to show or preserve theinformation about the location of occurrence/participation of a lowerorder OS into a higher order OSs, or to account for a number ofoccurrences of a lower OS in a higher OS etc., or any other desirableway of mapping/converting or conserving some or all of the informationof a composition into a participation matrix. In light of the presentdisclosure such cases can also be readily dealt with, by those skilledin the art, by slight mathematical modifications of the disclosedmethods herein.

Furthermore, as pointed out before, those skilled in the art can store,process or represent the information of the data objects of the presentapplication (e.g. list of ontological subjects of various order, list ofsubject matters, participation matrix/ex, association strengthmatrix/ex, and various types of associational, relational, novel,matrices, various value significance measures, co-occurrence matrix,participation matrices, and other data objects introduced herein) orother data objects as introduced and disclosed in the incorporatedreferences (e.g. association value spectrums, value significancemeasures, ontological subject map, ontological subject index, list ofauthors, and the like and/or the functions and their values, associationvalues, counts, co-occurrences of ontological subjects, vectors ormatrix, list or otherwise, and the like etc.) of the present inventionin/with different or equivalent data structures, data arrays or formswithout any particular restriction.

For example the PMs, ASMs, OSM or co-occurrences of the ontologicalsubjects etc. can be represented by a matrix, sparse matrix, table,database rows, no sql databases, JSON, dictionaries and the like whichcan be stored in various forms of data structures. For instance eachpart, section, or any subset of the objects of the current disclosuresuch as a PM, ASM, OSM, RNVSM, NVSM, and the like or the ontologicalsubject lists and index, or knowledge database/s can be representedand/or stored in one or more data structures such as one or moredictionaries, one or more cell arrays, one or more row/columns of an SQLdatabase, or by any implementation of No SQL database/s of differenttechnologies or methods etc., one or more filing systems, one or morelists or lists in lists, hash tables, tuples, string format, zip format,sequences, sets, counters, JSON, or any combined form of one or moredata structure, or any other convenient objects of any computerprogramming languages such as Python, C, Perl, Java, JavaScript etc.Such practical implementation strategies can be devised by variouspeople in different ways.

The detailed description, herein, therefore describes exemplary way(s)of implementing the methods and the system of the present invention,employing the disclosed concepts. They should not be interpreted as theonly way of formulating the disclosed concepts, algorithms, and theintroducing mathematical or computer implementable objects, measures,parameters, and variables into the corresponding physical apparatusesand systems comprising data/information processing devices and/or units,storage device and/or computer readable storage media, data input/outputdevices and/or units, and/or data communication/network devices and/orunits, etc.

The processing units or data processing devices (e.g. CPUs) must be ableto handle various collections of data. Therefore the computing or dataprocessing units to implement the system have compound processing speedequivalent of one thousand million or larger than one thousand millioninstructions per second and a collective memory, or storage devices(e.g. RAM), that is able to store large enough chunks of data to enablethe system to carry out the task and decrease the processing timesignificantly compared to a single generic personal computer availableat the time of the present disclosure.”

The data/information processing or the computing system that is used toimplement the method/s, system/s, and teachings of the present inventioncomprises storage devices with more than 1 (one) Giga Byte of RAMcapacity and one or more processing device or units (i.e. dataprocessing or computing devices, e.g. the silicon based microprocessor,quantum computers etc.) that can operate with clock or instructionspeeds of higher than 1 (one) Giga Hertz or with compound processingspeeds of equivalent of one thousand million or larger than one thousandmillion instructions per second (e.g. an Intel Pentium 3, Dual core, i3,i7 series, and Xeon series processors or equivalents or similar fromother vendors, or equivalent processing power from other processingdevices such as quantum computers utilizing quantum computing devicesand units) are used to perform and execute the method once they havebeen programmed by computer readable instruction/codes/languages orsignals and instructed by the executable instructions. Additionally, forinstance according to another embodiment of the invention, the computingor executing system includes or has processing device/s such asgraphical processing units for visual computations that are forinstance, capable of rendering, synthesizing, and demonstrating thecontent (e.g. audio or video or text) or graphs/maps of the presentinvention on a display (e.g. LED displays and TV, projectors, LCD, touchscreen mobile and tablets displays, laser projectors, gesture detectingmonitors/displays, 3D hologram, and the like from various vendors, suchas Apple, Samsung, Sony, or the like etc.) with good quality (e.g. usinga NVidia graphical processing units).

Also the methods, teachings and the application programs of the presentsinvention can be implement by shared resources such as virtualizedmachines and servers (e.g. VMware virtual machines, Amazon ElasticBeanstalk, e.g. Amazon EC2 and storages, e.g. Amazon S3, and the likeetc. Alternatively specialized processing and storage units (e.g.Application Specific Integrated Circuits ASICs, field programmable gatearrays (FPGAs) and the like) can be made and used in the computingsystem to enhance the performance and the speed and security of thecomputing system of performing the methods and application of thepresent invention.

Moreover several of such computing systems can be run under a cluster,network, cloud, mesh or grid configuration connected to each other bycommunication ports and data transfers apparatuses such as switches,data servers, load balancers, gateways, modems, internet ports,databases servers, graphical processing units, storage area networks(SANs) and the like etc. The data communication network to implement thesystem and method of the present invention carries, transmit, receive,or transport data at the rate of 10 million bits or larger than 10million bits per second;”

Furthermore the terms “storage device, “storage”, “memory”, and“computer-readable storage medium/media” refers to all types ofno-transitory computer readable media such as magnetic cassettes, flashmemories cards, digital video discs, random access memories (RAMS s),Bernoulli cartridges, optical memories, read only memories (ROMs), Solidstate discs, and the like, with the sole exception being a transitorypropagating signal.”

The detailed description, herein, therefore uses a straightforwardmathematical notions and formulas to describe exemplary ways ofimplementing the methods and should not be interpreted as the only wayof formulating the concepts, algorithms, and the introduced measures andapplications. Therefore the preferred or exemplary mathematicalformulation here should not be regarded as a limitation or constituterestrictions for the scope and sprit of the invention which is toinvestigate the bodies of knowledge and compositions with systematicdetailed accuracy and computational efficiency and thereby providingeffective tools, products and application in knowledge discovery,scoring/ranking, decision making, navigation, conversing, man/Machinecollaboration and interaction, filtering or modification of partitionsof a body of knowledge, string processing, information processing,signal processing and the like.

Having constructed the PM^(kl), we now launch to explain the methods ofdefining and evaluating the “value significances” of the ontologicalsubjects of the compositions for various important measures ofsignificance. One of the advantages and benefits of transforming theinformation of a composition into participation matrices is that once weattribute something to the OSs of particular order then we can evaluatethe merit of OSs of another order in regards to that attribute using thePMs. For instance, if we find words of particular importance in atextual composition then we can readily find the most importantsentences of the composition wherein the most important sentencescontain the most important words in regards to that particularimportance measure or aspect. Moreover, as will be shown, thecalculations become straightforward, language independent andcomputationally very efficient making the method practical, accurate tothe extent of our definitions, and scalable in investigating largevolumes of data or large bodies of knowledge.

The investigation method/s and the algorithm/s are now explained in thefollowing sections and subsections with the step by step formulationsthat is easy to implement by those of ordinary skilled in the art and byemploying computer programming languages and computer hardware systemsthat can be optimized or customized by build or hardware design toperform the algorithm efficiently and produce useful outputs for variousdesired applications.

II-II Value Significance Measures

This section begins to concentrate on value significance evaluation of apredetermined order OSs by several exemplary embodiments of thepreferred methods to evaluate the value of an OS of the predeterminedorder, within a same order set of OSs of the composition, for thedesired measure of significance.

Using these mathematical objects various measures of value significancesof OSs in a body of knowledge or a composition (called “valuesignificance measure”) can be calculated for evaluating the valuesignificances of OSs of different orders of the compositions ordifferent partitions of a composition. Furthermore, these variousmeasures (usually have intrinsic significances) are grouped in differenttypes and number to distinguish the variety and functionalities of thesemeasures.

The first type of a “value significance measure” is defined as afunction of “Frequency of Occurrences” of OS_(i) ^(k) is called hereFO_(i) ^(k|l) and can be given by:

vsm_1_(i) ^(k|l)=ƒ₁(FO_(i) ^(k|l)),i=1,2, . . . N  (6)

wherein FO_(i) ^(k|l) is obtained by counting the occurrences of OSs ofthe particular order, e.g. counting the appearances of particular wordin the text or counting its total occurrences in the partitions, or moreconveniently be obtained from the COM^(k|l) (the elements on the maindiagonal of the COM^(k|l)) or by using Eq. 4, or any other way ofcounting the occurrences of OS_(i) ^(k) in the desired partitions of thecomposition.

Moreover the ƒ₁ in Eq. 6 is a predetermined function such that ƒ₁(x)might be a liner function (e.g. ax+b), a power of x function (e.g. x³ orx^(0.53)) a logarithmic function (e.g. 1/log 2(x)), or 1/x function,etc.

Accordingly, a vsm_1_1_(i) ^(k|l), (stands for number one of type one“value significance measure”) for instance, can be defined as:

vsm_1_1_(i) ^(k|l) =c·FO_(i) ^(k|l)  (7)

wherein c is a constant or a pre-assigned vector. The vsm_1_1_(i) ^(k|l)of Eq. 7 gives a high value to the most frequent OS^(k). In anothersituation or some applications if, for a desired aspect, less frequentOSs are of more significance one may use the following vsm_1_2_(i)^(k|l) (number 2 of type 1 vsm)

$\begin{matrix}{{{{vsm\_}1\_ 2_{i}^{k|l}} = \frac{c}{\left( {FO}_{i}^{k|l} \right)}},{i = 1},2,{\ldots \mspace{14mu} N}} & (8)\end{matrix}$

Furthermore, another type of vsm_x_(i) ^(k|l) is defined as a functionof the “Independent Occurrence Probability” (IOP) in the partitions suchas:

vsm_2_(i) ^(k|l)=ƒ₂(iop_(i) ^(k|l)),i=1 . . . N  (9)

wherein the independent occurrence probability (iop_(i) ^(k|l)) mayconveniently be given by:

$\begin{matrix}{{\left( {iop}_{i}^{k|l} \right) = \frac{{FO}_{i}^{k|l}}{M}},{i = {1\mspace{14mu} \ldots \mspace{14mu} N}}} & (10)\end{matrix}$

and ƒ₂ is a predetermined function. For instance a vsm_2_1_(i) ^(k|l)(i.e. the number 1 type 2 vsm) can be defined as:

vsm_2_1_(i) ^(k|l)=−log₂(iop_(i) ^(k|l)),i=1 . . . N  (11)

This measure gives a high value to those OSs of order k of thecomposition (e.g. the words when k=1) conveying the most amount ofinformation as a result of their occurrence in the composition. Extremevalues of this measure can point to either novelty or noise.

Still, another type of vsm_x_(i) ^(k|l) is defined as a function of the“co-occurrence of an OS^(k) with others as:

vsm_3_(i) ^(k|l)=ƒ₃(com_(ij) ^(k|l)),i=1 . . . N  (12)

wherein the com_(ij) ^(k|l) is the co-occurrences of OS_(i) ^(k) andOS_(j) ^(k) and ƒ₃ is a predetermined function. For instance a vsm_3_(i)^(k|l) can be defined as:

vsm_3_1_(i) ^(k|l)=ƒ₃(com_(ij) ^(k|l))=Σ_(j)com_(ij) ^(k|l) ,i=1 . . .N  (13).

This measure gives a high value to those frequent OSs of order k thathave co-occurred with many other OSs of order k in the partitions oforder l.

This measure (Eq. 13) once combined with other measures can yet provideother measures. For instance when it is being divided by the vsm_1_1_(i)^(k|l) of Eq. 7, (e.g. being divided by FO_(i) ^(k|l)), the resultantmeasure can indicates the diversity of occurrence of that OS. Therefore,this particular combined measure usually gives a high value to thegeneric words (since generic words can occur with many other words).Once the generic words excluded from the list of OSs of the order k thenthis measures can quickly identifies the main subject matter of acomposition so that it can be used to label a composition or forclassification, categorization, clustering, etc.

Accordingly, more vsm_x_(i) ^(k|l) can be defined using the one or moreof the other vsm_(i) ^(k|l) or the variables. For instance one candefine a vsm_x_(i) ^(k|l) of type 4 (x=4) as function of vsm_1_2_(i)^(k|l) given by Eq. 8 and com_(ij) ^(k|l) as the following:

vsm_4_1_(i) ^(k|l)=ƒ₄(vsm_1_2_(i) ^(k|l),com_(ij) ^(k|l))=Σ_(i)(com_(ij)^(k|l)·vsm_1_2_(i) ^(k|l))=(1/FO_(i) ^(k|l))^(T)×COM,i,j=1 . . . N  (14)

wherein “T” stands for matrix or vector transposition operation andwherein we substitute the vsm_1_2_(i) ^(k|l) from Eq. 8 into Eq. 12 or14. This measure also points to the diversity of the participations ofthe respective OS especially when COM is made digital.

For mathematical accuracy it is noticed that in our notation the index“i” refers to the row number and the index “j” refers to the columnnumber therefore the matrices with only the subscript of “i” usually arethe column vectors and the matrices with only the subscript of “j”usually are row vectors.

In a similar fashion there could be defined, synthesized, and becalculated various vsm_x_(i) ^(k|l) (x=1, 2, 3, . . . ) vectors forOS_(i) ^(k) that are indicatives of one or more significances aspect'sof an OS_(i) ^(k) in the composition or the BOK. These groups ofvsm_x_(i) ^(k|l) generally refer to the intrinsic value significance ofan OS in the BOK.

These “value significance measures” (vsm_x_(i) ^(k)) are more indicativeof intrinsic importance or significances of lower order constituent partthat can be use to separate one or more of the these OSs for variety ofapplications such as labeling, categorization, clustering, buildingmaps, conceptual maps, ontological subject maps, or finding othersignificant parts or partitions of the composition or the BOK. Forinstance as disclosed in the incorporated references the vsm_x_(i)^(k|l) can readily be employed to score a set of document or to selectthe most import parts or partitions of a composition by providing thetools and objects to weigh the significances of parts or partitions of aBOK.

Accordingly, from the vsm_x_(i) ^(k) vectors one can readily proceed tocalculate the vsm_x of other OS of different order (i.e. an order l)utilizing the participation matrices PM^(kl) by a multiplicationoperation by:

vsm_x _(j) ^(l|kl)=(vsm_x _(i) ^(k))^(T)×pm_(ij) ^(kl) j=1,2, . . . Mand i=1,2, . . . N  (15)

wherein vsm_x_(j) ^(l|kl) is the type x value significance of OSs oforder l obtained from the data of the PM^(kl). An instance meaning of OSof order l for a textual composition or a BOK is a sentence (e.g. l=2),a paragraph (e.g. l=3) or a document (l=5). The vsm_x_(j) ^(l|kl)thereafter can be utilized for scoring, ranking, filtering, and/or beused by other functions and applications based on their assigned valuesignificances.

Generally, many other “value significant measures” can be constructed orsynthesized as functions of other “value significance measures” toobtain a desired new value significance measure.

Therefore, from the disclosure here, it becomes apparent as how variousfiltering functions can be synthesized utilizing the participationmatrix information of different orders and other derivative mathematicalobjects. The method is thereby easily implemented and is processefficient.

An immediate application of the theory and the associated methods,systems, and applications are instrumental in processing of naturallanguages composition and building the artificial intelligences capableof interacting with humans in an intelligent manner.

II-III the Association Strength

This section look into another important attributes of the ontologicalsubjects of a composition that is instrumental and desirable ininvestigating the composition of ontological subjects.

According to the theoretical discoveries, methods, systems, andapplications of the present invention, the concept and evaluationmethods of “association strengths” between the ontological subjects of acomposition or a BOK play an important role in investigating, analyzingand modification of compositions of ontological subjects.

Accordingly, the “association strength measures” are introduced anddisclosed here. The “association strength measures” play importantrole/s in many of the proposed applications and also in calculating andevaluating the different types of “value significance evaluation” of OSsof the compositions. The values of an “association strength measure” canbe shown as entries of a matrix called herein the “Association StrengthMatrix (ASM^(k|l))”.

The entries of ASM^(k|l) is defined in such a way to show the conceptand rational of association strength according to one exemplary generalembodiment of the present invention as the following:

asm_(i→j) ^(k|l)=ƒ(com_(ij) ^(k|l),vsm_x _(i) ^(k),vsm_y _(j) ^(k)) . .. i,j=1 . . . N,x,y=1,2, . . .  (16),

where asm_(i→j) ^(k|l) is the “association strength” of OS_(i) ^(k) toOS_(j) ^(k) of the composition and ƒ is a predetermined or a predefinedfunction, com_(ij) ^(k|l) are the individual entries of the COM^(k|l)showing the co-occurrence of the OS_(i) ^(k) and OS_(j) ^(k) in thepartitions or OS^(l), and the vsm_x_(i) ^(k) and vsm_y_(j) ^(k) are thevalues of one of the “value significance measures” of type x and type yof the OS_(i) ^(k) and OS_(j) ^(k) respectively, wherein the occurrenceof OS^(k) is happening in the partitions that are OSs of order l.Usually the vsm_x_(i) ^(k) and/or the vsm_y_(j) ^(k) are the same asvsm_x_(i) ^(k|l) and/or the vsm_y_(j) ^(k|l) which means it has beencalculated from the participation data of the OS^(k) in the OSs of orderl.

Accordingly having selected the desired form of the function ƒ andintroducing the exemplary quantities from Eq. 6, and/or 9 and/or Eq. 12into Eq. 16 the value of the corresponding “association strengthmeasure” can be calculated.

Referring to FIG. 2 here, it shows one definition for association of twoor more OSs of a composition to each other and shows how to evaluate thestrength of the association between each two OSs of composition. In FIG.2 the “association strength” of each two OSs has been defined as afunction of their co-occurrence in the composition or the partitions ofthe composition, and the value significances of each one of them.

FIG. 2, moreover shows the concept and rational of this definition forassociation strength according to this disclosure. The larger andthicker elliptical shapes are indicative of the value significances,e.g. probability of occurrences, of OS_(i) ^(k) and OS_(j) ^(k) in thecomposition that were driven from the data of PM^(kl) and wherein thesmall circles inside the area is representing the OS^(l) s of thecomposition. The overlap area shows the common OS^(l) between the OS_(i)^(k) and OS_(j) ^(k) in which they have co-occurred, i.e. thosepartitions of the composition that includes both OS_(i) ^(k) and OS_(j)^(k). The co-occurrence number is shown by com_(ij) ^(k|l), which is anelement of the “Co-Occurrence Matrix (COM)” introduced before (Eq. 5).

The various asm_(i→j) ^(k|l) can be grouped into types and number inorder to distinguish them from other measures in a similar fashion inlabeling and naming the VSMs in the previous subsection. Consequentlyfew exemplary types of “association strength measures”, asm_(i→j)^(k|l), are given below:

asm_1_1_(i→j) ^(k|l)=com_(ij) ^(k|l) . . . i,j=1 . . . N  (17)

$\begin{matrix}{{{{asm\_}2\_ 1_{i->j}^{k|l}} = {{{com}_{ij}^{k|l}/{vsm\_ x}_{i}^{k|l}}\mspace{14mu} \ldots \mspace{14mu} i}},{j = {1\mspace{14mu} \ldots \mspace{14mu} N}},x,{y = 1},2,\ldots} & (18) \\{{{{asm\_}3\_ 1_{i->j}^{k|l}} = {{\frac{{vsm\_ y}_{j}^{k|l}}{{vsm\_ x}_{i}^{k|l}} \cdot {com}_{ij}^{k|l}}\mspace{14mu} \ldots \mspace{14mu} i}},{j = {1\mspace{14mu} \ldots \mspace{14mu} N}},x,{y = 1},2,\ldots} & (19)\end{matrix}$

It is important to notice that the association strength defined by Eq.16, is not usually symmetric and generally

ask_(j− > i)^(k|l) ≠ asm_(i− > j)^(k|l).

Therefore, one important aspect of the Eq. 16 to be pointed out here isthat associations of OSs of the compositions are not necessarilysymmetric and in fact an asymmetric “association strength measure” ismore rational and better reflects the actual semantic relationshipsituations of OSs of the composition.

For instance in the patent application Ser. No. 12/939,112 the exemplaryand preferred “association strength measure” that in this application islabeled as asm_3_2_(i→j) ^(k|l), (it reads as number 2 type 3“association strength measure”) to make it distinguishable from othermeasures, was defined as:

$\begin{matrix}{{{{asm\_}3\_ 2_{i->j}^{k|l}} = {{c\; \frac{{com}_{ij}^{k|l}}{\left( {{iop}_{i}^{k|l}/{iop}_{j}^{k|l}} \right)}} = {c\; \frac{{com}_{ij}^{k|l} \cdot {iop}_{j}^{k|l}}{{iop}_{i}^{k|l}}}}},i,{j = {1\mspace{14mu} \ldots \mspace{14mu} N}}} & (20)\end{matrix}$

where c is a predetermined constant, or a pre-assigned value vector, ora predefined function of other variables in Eq. 20, com_(ij) ^(k|l) arethe individual entries of the COM^(k|l) showing the co-occurrence of theOS_(i) ^(k) and OS_(j) ^(k) in the partitions of order l, and theiop_(i) ^(k|l) and iop_(j) ^(k|l) are the “independent occurrenceprobability” of OS_(i) ^(k) and OS_(j) ^(k) in the partitionsrespectively, wherein the occurrence is happening in the partitions thatare OSs of order l. In a particular case, it can be seen that in Eq. 20,the un-normalized “association strength measure” of each OS with itselfis proportional to its frequency of occurrence (or self occurrence).

This exemplary choice of definition for “association strength measure”,i.e. Eq. 20, is further illustrated here. In fact Eq. 20 basicallystates that if a less popular OS co-occurred with a highly popular OSthen the association of the less poplar OS to the highly popular OS ismuch stronger than the association of the highly popular OS with theless popular OS (remembering the co-occurrence is a symmetric). Thatmake sense, since the popular OSs obviously have many associations andare less strongly bounded to anyone of them so by observing a highpopular OSs one cannot gain much upfront information about theoccurrence of less popular OSs. However observing occurrence of a lesspopular OSs having strong association to a popular OS can tip theinformation about the occurrence of the popular OS in the samepartition, e.g. a sentence, of the composition.

In another instance it may be more desirable to have defined theassociation strength measure as:

$\begin{matrix}{{{{asm\_}2\_ 2_{i->j}^{k|l}} = {c\; \frac{{com}_{ij}^{k|l}}{{iop}_{i}^{k|l}}}},i,{j = {1\mspace{14mu} \ldots \mspace{14mu} N}}} & (21)\end{matrix}$

This asm_2_2_(i→j) ^(k|l) measure effectively expressing thatassociation of an OS_(i) ^(k) to another one, say OS_(j) ^(k), isstronger when the co-occurrences of them is high and the probability ofoccurrence of OS_(i) ^(k) is low. In other words if an OS is occurringless frequently and whenever it has occurred it has appeared more oftenwith one particular OS then the association bond of the less frequentlyoccurring OS is strongest with the particular OS that has co-occurredwith, the most. In the other way for a given co-occurrence number for aparticular OS, say OS_(j) ^(k), it's highest associated bond is from theOS with less independent occurrence probability. Mathematically, infact, the asm_2_2_(i→j) ^(k|l) is the column normalized version of theasm_3_2_(i→j) ^(k|l) of Eq. 20 (when c=1/M in Eq. 21 and assuming binaryPM) and is more useful in some instances and applications.

This particular association strength measure can reveal a strongrelationship from a less significant OS to the one who has co-occurredthe most and is a useful measure to hunt for some types of novelty.

Yet in another instance an application/s is found for the followingassociation strength definition:

asm_4_1_(i→j) ^(k|l) =c·com_(ij) ^(k|l)·iop_(j) ^(k|l) i,j=1 . . .N  (22).

The asm_4_1_(i→j) ^(k|l) attributes the strongest association bond froma first OS, say OS_(i) ^(k), to a second OS, say OS_(j) ^(k), when theproduct of their co-occurrences and the independent probability ofoccurrence of the second OS is the highest. This association strengthmeasure usually is useful for discovering the real association of twoimportant or significant OSs of the composition.

And yet further, this measure can be defined to hunt for mutualassociations bonds such as word phrases as the following:

$\begin{matrix}{{{{asm\_}2\_ 3_{i->j}^{k|l}} = {c\; \frac{\left( {com}_{ij}^{k|l} \right)^{2}}{{Fo}_{i}^{k|l} \cdot {Fo}_{j}^{k|l}}}},i,{j = {1\mspace{14mu} \ldots \mspace{14mu} N}}} & (23)\end{matrix}$

This measure of association strength (i.e. Eq. 23) is symmetric andgives a high value to those pairs of OSs that frequently co-occur witheach other such as word phrases. This becomes equal to 1 (assuming c=1in Eq. 23) when two words have always co-occurred with each other.

These are few exemplary but useful types of association strengthmeasures which are found to be instrumental in analyzing andinvestigation of a composition of ontological subjects. However by Eq.16 it can be seen that there could be defined, synthesized and calculatenumerous other association strength measures. Furthermore consideringthat com_(ij) ^(k|l) is also one type of “association strength measure”therefore Eq. 16 can be further generalized as:

asm_x2_(i→j) ^(k|l) =F(asm_x1_(i→j) ^(k|l),vsm_x _(i) ^(k),vsm_y _(j)^(k)) . . . i,j=1 . . . N,x,y=1,2, . . . ,x1,x2=1,2, . . .  (24),

wherein F is a predetermined function and x1 and x2 refer to differenttypes of association strength measures and x_(i) and y_(j) refer to oneof the “value significance measures” of the different types of “valuesignificance measures”. To illustrate this, one can see that theasm_3_2_(i→l) ^(k|l) can be expressed versus the asm_2_2_(i→l) ^(k|l)(Eq. 21) and the vsm_1_(j) ^(k|l) (Eq. 7) as:

asm_3_2_(i→l) ^(k|l) =c·asm_2_2_(i→l) ^(k|l)·vsm_1_(j) ^(k|l)  (25)

wherein c is a constant and “·” indicates an element-wise multiplicationof two vectors and wherein Eqs. 7, 10, 20, 21 were combined to derivethe Eq. 25.

These illustrating examples are given to demonstrate that with theconcept of “value significance” and “association strengths” there willbe various ways to synthesize, perform, calculate and obtain the desiredassociation strength for the particular application by those skilled inthe art.

II-III-I-Cross Association Strength Measures

Also importantly from the one or more of the “association strengthmeasures” one can go on and define a measure for evaluating the hiddenassociation strength of OS of order k even further by:

ASM_x3^(k|l)=(ASM_x1^(k|l))^(T)×ASM_x2^(k|l)  (26)

wherein ASM_x3^(k|l) stands for type x3 “association strength measure”which is basically a N×N matrix. The Eq. 26 takes into account thetransformative or hidden association of OSs of order k (e.g. words of atextual composition or BOK) from one asm measure and combines with theinformation of another or the same asm measure to gives another measureof association that is not very obvious or apparent from the start. Thistype of measure therefore takes into account the indirect or secondaryassociations into account and can reveal or being used to suggest new orhidden relationships between the OSs of the compositions and thereforecan be very instrumental in knowledge discovery and research.

Eq. 26 can, in fact, be interpreted as “cross-association strength”between ontological subjects in general with the same or differentassociation strength measure in mind.

When we use the same type of association strength measure, in yetanother exemplary and effective way we introduce another measure ofassociation calling it “cross-association strength measure” or CASM forshort which is defined as:

CASM=(ASM×ASM^(T))  (26.1)

Wherein, in here, ASM, is one of the desired types of the associationmatrix and “T” stands for matrix transposition operation and “x”indicates matrix multiplications. Eq. 26.1 is one particular case forthe general concept of “cross-association strength measures” which isdescribed, defined, represented, and calculated by Eq. 26. It isunderstood that CASM (or any other objects of mathematical and dataobjects this disclosure) can further be processed or go through othermathematical operations when desired.

It is worth mentioning again (as mentioned before or in the incorporatedreferences), that all the data objects of present disclosure and thecorresponding matrixes vectors etc. can be made to become normalizedThat is for instance, any desired matrix of this disclosure can be, andvery frequently desirable, to become column normalized, or rownormalized (i.e. the norm or the length of each column or row of thedesired matrix is unity). Further the multiplications and/or products ofthe matrices, sometime are element-wise and sometimes are inner productsand sometimes are normalized inner products of the vectors of thecorresponding Hilbert space. For instance

A very important, useful, and quick use of exemplary “associationstrength measures” of Eq. 17-26 and 26.1 is to find the real associatesof a word, e.g. a concept or an entity, from their pattern of usage inthe partitions of textual compositions. Knowing the associates of words,e.g. finding out the associated entities to a particular entity ofinterest, finds many applications in the knowledge discovery andinformation retrieval. In particular, one application is to quickly geta glance at the context of that concept or entity or the wholecomposition under investigation. The choice and the evaluation method ofthe association strength measure is important for the desiredapplication. Furthermore, these measures can be directly used as adatabase of semantically associated words or OSs in meaning or semantic.For instance if the composition under investigation is the entire (oreven a good part of) contents of Wikipedia, then universal associationof each entity (e.g. a word, concept, noun, etc.) can be calculated andstored for many other applications such as in artificial intelligence,information retrieval, knowledge discovery and numerous others.

Moreover, from the “association strength measures” one can also obtainand derive various other “value significance measures” which poses moreof intrinsic type of significances. For instance in the application Ser.No. 12/939,112 the asm_(i→j) ^(k|l) (e.g. Eq. 20-26) was used to defineand calculate few exemplary “value significance measures”, i.e. vsm_(i)^(k|l), in order to evaluate the intrinsic importance, credibility, andimportance of OSs of different orders.

In practice, for given a OS, e.g. OS_(j) ^(k), we want to find out thestrongest “associated with” OS (assume it found out to be the OS_(i)^(k)). To do that we can use Eq. 21. Also one can use the Eq. 22 to findout which OS the given OS, say OS_(i) ^(k), is highly “associated to”(assume it was found out to be the OS_(j) ^(k)).

To find out the semantically or functionally related OSs one can use Eq.26 which is an important tool for knowledge discovery. For instance thismeasure can be used to hunt for the subject matters that can in fact behighly related, but one cannot find their relations in the literatureexplicitly. The “association strength measure” of Eq. 26, thereby canpoint to interesting and important topics of further investigation orresearch either by human researcher or an intelligent machine.

In the next subsection the rational and definition of yet other types ofinstrumental measures and way of calculating them are given

II III II Relational Association Measures

As mentioned above the association strength values are important formany applications. One or more of such applications is to cluster or tofind hidden relationships between the partitions of the compositions.The asm_(i→j) of the lower order OSs can show the association strengthof the higher order OSs of the composition thereby to use them forclustering, categorization, scoring, ranking and in general filteringand manipulating the higher order OSs.

Accordingly, in this section we further disclose and explain the conceptof “Relational Association Strength measure” (RASM). In the generalterms, from lower order “association strength matrix” we can proceed tocalculate association strength of higher order OSs to a lower order OSthat we call it “Relational Association Strength measure” (RASM) here.

One exemplary instance of such “Relational Association Strength measure”can be given by:

RASM_1^(l→k|kl)=rasm_1_(i) _(l) _(j) _(k)^(l→k|kl)=(PM^(kl))^(T)×ASM^(k|l) i _(l)=1,2, . . . M and j _(k)=1,2, .. . N  (27)

wherein rasm_1_(i) _(l) _(j) _(k) ^(l→k|kl) or the RASM_1^(l→k|kl) isthe “first type relational association strength measure” of OSs of orderl to OSs of order k, which is a M×N matrix and shows the degree that anOS of order l (e.g. the i_(l)th sentence of the composition) isassociated or is related to a particular OS of order k (e.g. to thej_(k)th word of the composition).

It is noted that ASM^(k|l) is generally a square asymmetric matrix,whose transpose is not equal to itself, and therefore there could beenvisioned another, also important, type of “relational associationstrength measure”. Accordingly, in the same manner the “second typerelational association strength measure” can be defined and calculatedas:

RASM_2^(l→k|kl)=rasm_2_(i) _(l) _(j) _(k)^(l→k|kl)=(PM^(kl))^(T)×ASM^(k|l) ^(T) i _(l)=1,2, . . . M and j_(k)=1,2, . . . N  (28)

wherein rasm_2_(i) _(l) _(j) _(k) ^(l→k|kl) or the RASM_2^(l→k|kl) isthe “second type relational association strength measure” of OSs oforder l to OSs of order k, which is also a M×N matrix and is similar toRAS_M^(l→k|kl) except relational emphasis is from different aspect. Forinstance if the ASM used in Eq. 28 is from the Eq. 20, then for a givenOS of order k (e.g. a particular keyword) the RASM_1^(l→k|kl) shows ahigh relatedness for those partitions (e.g. sentences or paragraphsetc.) that contain the words that are highly bonded to the target OS.Whereas at the same condition using the RASM_2^(l→k|kl) then thosesentences that contain the words that the target OS is highly associatedwith show a strong relatedness to the target OS.

Therefore using the above relational rasm one can conveniently find themost related partitions of a composition to one or more target OS forthe desired goal of the investigation (e.g quick retrieval of documents,sentences, or paragraphs with high semantic relevancy).

On the other way, the RASM_2^(l→k|kl) or RASM_1^(l→k|kl) can be usedalso to find out the association strength or relatedness of particularOS of order k (e.g. the j_(k)th word of the composition) to a particularOS of order l (e.g. the i_(l)th sentence of the composition) by havingthe following relationship:

RASM_x ^(k→l|kl)=(RASM_x ^(l→k|kl))^(T)  (29).

The reason that the present invention call RASM_x^(l→k|kl) “RelationalAssociation Strength Measure” of type x, is to remind the fact thatthese types of association strength are not only between a higher orderOS (e.g. a sentence, paragraph, or a document, or a segment/partitionsof a picture) with a lower order OS (e.g. a word or a keyword, phrase, apixel, or section of a picture etc) but it is, in an indirect way, alsobetween a higher order OS and the associations of a lower order OS. Thename for the other way around relationship (i.e. RASM_x^(k→l|kl)) isalso appropriate in which not only a lower order OS is associated with ahigher order OS but also is related to other constituent lower order OSsof the higher order OS.

Many more useful mathematical objects and relations are obtained, in asimilar fashion as thought in the present invention, from which varietyof operations can be envisioned. For instance we can proceed tocalculate the association strength between the OSs of order l (e.g. anassociation strength measure between sentences of a textual composition)by the following:

RASM_x ^(l→l|kl)=rasm_x _(i) _(l) _(j) _(l) ^(l→l|kl)=RASM_x^(l→k|kl)×RASM_x ^(k→l|kl) ,i _(l) ,j _(l)=1,2, . . . M   (30)

wherein rasm_x_(j) _(k) ^(l→l|kl) is indicative of one type of“relational association strength measure” between ith OS of order l andjth OS of order l. This matrix is particularly useful to find or selectthe higher order OSs of the composition or the partitions (e.g.sentences or paragraphs, or documents), that are highly associated witheach other. In some applications, though, it would be desirable, forinstance, to find out the partitions that have the least amount ofassociations with any other partitions etc.

In general one or more of these “related associations measures” can beused (either normalized or not) to define and/or synthesize new RASMs.

By the same manner using “Participation Matrix/es” and other objects,other desired features can be quantified in a composition or a BOK andconsequently make it possible to select, clustered, or filter out thedesired part or parts of the composition to look into, investigate,modified, re-composed, etc.

Eqs. 27-30 make it easy to find the partitions of the compositions thathave the highest relatedness or highest relative association with akeyword or the other way around etc. Therefore a computer implementedmethod utilizing these formulations can essentially filters out the mostrelated parts or partitions of a composition in relation to a targetkeyword.

One immediate application, of course, is for scoring the relatedness ofgroup of documents to a subject matter or a keyword. Another immediateapplication of the computer implemented method, utilizing the concept ofRASM_x^(l→k|kl) and the formulation, for instance, is to cluster andseparate partitions of a BOK or a large corpus/s, etc into sets ofpartitions that are related to a particular subject matter. Therelatedness is measured by one or more of the above measures andpartitions that exhibited an association strength value greater (orsometimes smaller) than a predetermined threshold to a particular OS,can be grouped or clustered together. Further these data can be readilyused to build a neural network type system (for learning, reasoningetc.) whose edge/connection weights can be obtained from the data ofassociation strengths of the ontological subjects (e.g. the node of aneural net). In this way the training of a neural net can be done muchfaster or simply by reading a body of knowledge to attain the necessarydata for building a learnt (e.g. adjusted weight by training throughobserving output/input as done currently without the teachings of thethis disclosure) neural net. The association strength data structuresusually in the form a matrix therefore is instrumental to build suchcognitive networks for variety of tasks in general and for buildingneural nets in particular. The training iteration and the resourceneeded to train a neural net is significantly reduced using theinformation of the association strengths (and various other data objectsor data structures introduced in this disclosure) of the ontologicalsubjects obtained by investigating a body of knowledge as taught throughthis disclosure.

In light of the foregoing explanation, the algorithm and method ofclustering become straightforward. For instance, a number of partitionsof the composition or the BOK that have exhibited a predeterminedthreshold of relative association strength or predetermined criteria ofsatisfying enough association strength to a target subject or to eachother can be categorized or being clustered as group together.

As a practical example, these method/s, were successfully andeffectively used for clustering and categorizing a large of number ofnews feeds as shown in FIG. 11 which will be explained in the nextsubsections (section II-II-I).

Nevertheless in the short note here, the FIG. 11 shows the procedure inwhich using the concept of “value significance” selected a number ofhead category are selected from those OSs exhibiting the highest valuesignificances, and consequently using the “related association strengthmeasure” concept it was possible to separate the very many differentnews feeds into different categories automatically with satisfactoryaccuracy.

In the next section, in accordance with another aspect of thisdisclosure the relative or “relational value significance measures”(RVSM) are further introduced to evaluated the relative significances ofvarious OSs in relation to a target OS in the context of the given BOK.

II-IV Relational Value Significance Measures

Considering the case wherein one is looking for an important partitionof the BOK related to a target OS (e.g. OS_(j) ^(k)) which could be aword or a phrase, subject matter, keyword etc. Consequently one needs avalue significance measure/s that is measured in relation or relative toone or more target OS. One can call this conceptual measure as“relational value significance measure” or RVSM.

In here the RVSM can simply be the association strengths of OS_(i) ^(k),i=1, 2, . . . N to a target OS_(j) _(k) ^(k), i.e. asm_(i→j) _(k) ^(k|l)or the j_(k)th column of the ASM^(k|l) matrix, which when is used as aUSM vector that can give a weighted importance of partitions of thecomposition or the BOK (i.e. an OS_(i) _(l) ^(l)) in relation to thetarget OS_(i) ^(k) when operates (multiply) on the participation matrixPM^(kl), as the following:

rvsm_1_x _(i) _(l) _(j) _(k) ^(l→k|kl)=(pm_(i) _(k) _(i) _(l)^(kl))^(T)×asm_y _(i) _(k) _(→j) _(k) ^(k|l) . . . i _(k) ,j _(k)=1,2, .. . N and i _(l)=1,2, . . . M and x,y=1,2, . . .  (31)

wherein rvsm_1_x_(i) _(l) _(j) _(k) ^(l→k|kl) stands for type 1 ofnumber x “relational value significance measure” of OSs of order l,OS_(i) _(l) ^(l), to a given OS_(j) _(k) ^(k) which is a row vector andis obtained by processing the participation data of OS^(k) in OS^(l) orin other words it has been driven from the data of PM^(kl) and y isindicative the type of the “association strength measure”.

For the sake of simplicity usually the x and y are the same type.Accordingly, as can be seen in this embodiment the first type“relational value significance measure”, rvsm_1_x_(i) _(l) _(j) _(k)^(l→k|kl), is in fact the same as rasm_1_x_(i) _(l) _(j) _(k) ^(l→k|kl)the “first type relational Association strength measure” introduced inEq. 27.

Eq. 31, once executed, will assign values to OS^(l) in which itamplifies the importance or significance values of the partitions (e.g.sentences) of the composition that contains the OSs (e.g. words) thathave the highest association strength to the target OS_(j) ^(k) (i.e. atarget keyword) thereby to provide an instrument, i.e. a filteringfunction, for scoring and consequently selecting one or more highlyrelated partitions to an OS_(j) ^(k).

In fact the Eq. 31 can also be written in a matrix form wherein thervsm_(i) _(l) _(j) _(k) ^(l→k|kl) is a M by N matrix indicating therelative importance of the partitions to each of OS_(j) ^(k). In otherwords rvsm_(i) _(l) _(j) _(k) ^(l→k|kl) is a kind of “relational valuesignificance measure” and can be used as, say, “first type relationalvalue significance measure” (e.g. can be shown by RVSM_1 notation).

The RVSM_1 therefore, following the Eqs. 27 and 31, can be given in thematrix form as:

RVSM_1_x ^(l→k|kl)=RASM_1^(l→k|kl)=rvsm_1_(i) _(l) _(j) _(k)^(l→k|kl)=(PM^(kl))^(T)×ASM^(k|l) ,i _(l)=1,2, . . . M and j _(k)=1,2, .. . N  (32)

wherein the “T” shows the transposition matrix operation andRASM_1^(l→k|kl) is the “Relational Association Strength Matrix” and theRVSM_1 is the “first type relational value significance measure”. It isnoticed that ASM^(k|l) is a N×N matrix and RASM_1^(l→k|kl) is a M×Nmatrix indicating the relatedness/association of OS_(i) ^(l) (e.g. asentence and i=1 . . . M) to a OS_(j) ^(k) (e.g. a word and j=1 . . .N).

In a similar fashion there could be defined a second type relative valuesignificance measure (e.g. can be shown by RVSM_2 notation).

as:

RVSM_2^(l→k|kl)=rvsm_1_(i) _(l) _(j) _(k)^(l→k|kl)=(PM^(kl))^(T)×(ASM^(k|l))^(T) i _(l)=1,2, . . . M and i_(k)=1,2, . . . N  (33)

Or equivalently (see Eq. 28) given by:

RVSM_2^(l→k|kl)=RASM_2^(l→k|kl)  (34)

wherein the RVSM_2^(l→k|kl) or the RASM_2^(l→k|kl) indicates therelatedness/association strength of OS_(i) ^(l) (e.g. a sentence and i=1. . . M) or its “relational value significance” to a OS_(j) ^(k) (e.g. aword and j=1 . . . N).

Remembering the ASM^(k|l) in general is asymmetric and have differentinterpretation in which the rows of ASM^(k|l) indicates the value ofassociation to other and column indicates the value of being associationwith by others. Therefore the RVSM_1^(l→k|kl) is indicative of a degreethat an OS of order l, OS_(i) ^(l), (e.g. sentences) containing the OSsof order k, OS^(k) (e.g. the words) that are used to explain or expressor provide information regarding the target OS_(j) ^(k) (i.e. containingthe words that are highly associated with the target OS). Whereas theRVSM_2^(l→k|kl) is indicative of a degree that an OS_(i) ^(l) (e.gsentences) containing the OS^(k) (e.g. the words) for which the targetOS_(i) ^(k) is used or participated to explain or express or provideinformation about them (i.e. containing the words that the target OS ishighly associated with).

Yet a third type of “relational value significance measure” can bedefined as:

RVSM_3_(i) _(l) _(j) _(k) ^(l→k|kl)=vsm_(jk)^(k|l)·RASM_1^(l→k|kl)=vsm_(j) _(k) ^(k|l)((PM^(kl))^(T)×ASM^(k|l))i_(l)=1,2, . . . M and j _(k)=1,2, . . . N  (35)

wherein “·” indicates an element-wise multiplication and the vsm_(jk)^(k|l) could be the value of the one of the “value significancemeasures”.

And yet “forth type relational value significance measure” can bedefined and calculated as:

RVSM_4_(i) _(l) _(j) _(k) ^(l→k|kl)=vsm_(jk)^(k|l)·RASM_2^(l→k|kl)=vsm_(j) _(k) ^(k|l)((PM^(kl))^(T)×ASM^(k|l))i_(l)=1,2, . . . M and j _(k)=1,2, . . . N  (36)

Therefore there could also be defined various “relational valuesignificance measures” by incorporating the “intrinsic valuesignificances” and the “relational association strength”.

Accordingly, in general the RVSM_x_(i) _(l) _(j) _(k) ^(l→k|kl) can berewritten as:

RVSM_x _(i) _(l) _(j) _(k) ^(l→k|kl)=ƒ_(x)(vsm_(j) _(k)^(k|l),RASM_1^(l→k|kl),RASM_2^(l→k|kl))  (37)

wherein RVSM_x_(i) _(l) _(j) _(k) ^(l→k|kl) is the “type x relationalvalue significance measure” and the ƒ_(x) is a predetermined function.

These measures, RVSM_3_(i) _(l) _(j) _(k) ^(l→k|kl) and/or RVSM_4_(i)_(l) _(j) _(k) ^(l→k|kl), put an intrinsically high value on thesignificance of the partitions that are highly related to the high valuesignificance OS^(k) of the composition by taking the intrinsic value ofthe target OSs into account. Therefore these measures can beinstrumental to, for example, representing a body of knowledge with thehighest relational value significance or to summarize a composition. Todo so one can simply select one or more partition of the BOK that scoredthe highest for these measures in order to present it as summary of acomposition.

Furthermore, from RVSM_x_(i) _(l) _(j) _(k) ^(l→k|kl) one can proceed tocalculate the “relational value significance measures” between the OSsof higher order l as:

RVSM_x ^(l→k|kl)=rvsm_x _(i) _(l) _(j) _(k) ^(l→k|kl)=RVSM_x^(l→k|kl)×(RVSM_x ^(l→k|kl))^(T) ,i _(l) ,j _(l)=1,2 . . . M  (38)

wherein RVSM_x^(l→k|kl) is the relative value significance measurebetween OSs of order l so that it can directly measure the relatednessof partitions of the BOK such as sentences, paragraphs, or documents toeach other. Again this measure therefore can readily be used to find thehighly related partitions of the BOK either for retrieval purposes,rankings, document comparisons, question answering, conversation, orclustering and the like.

The concept behind the “relational value significance measures” is forprocessing and investigating compositions of ontological subject as itbecome important in these investigations to have tools, measures, andfiltering functions and methods of building such filtering functions tospot a partition relevant to another part or partition or to a givencomposition or query.

For instance in the information retrieval it becomes increasinglyimportant to have retrieved the most relevant pieces of information andtherefore the retrieved documents or the parts thereof should be themost relevant document and partition to a target OS which could be akeyword or set of keywords or even a composition itself. For instance itwould be very useful and desirable to find the most relevant document orpiece of knowledge to an input query in the form of a natural languagequestion, or even a paragraphs or a whole text document. In thisparticular application one or more of the various kind and types of the,so far introduced, “value significance measures” can readily be appliedusing the method of this discloser to retrieve and present the mostrelevant part (e.g. a word, a sentence, a paragraph, a chapter, adocument) to the sought after subject matter or in response to a query.

Many other desirable outcome and functionality can be built in light ofthe teachings and the disclosed method of systematic andcomputer-implementable methods of investigations not only for textualcompositions but also for other types of compositions. In fact thedisclosed method has been used and applied on image and videocompositions as well as genetic code compositions which confirmed themethod/s is indeed very effective in investigating compositions ofontological subject to obtain a desirable outcome or information orknowledge or the result.

In another aspect of the present invention, in the next section, are theconcept and definitions of “novelty value significance measures” (NVSM),as indication of various situations of novelty of OSs in the compositionor the BOK.

II-V-Novelty Value Significance Measures

According to another aspect of investigation methods of compositions yetother value significance measures are introduced and explored herein.According to this aspect of investigation, in some instances it wouldbecome desirable to have found the words or the partitions of acomposition expressing novel information about one or more subjectmatter/s. In these instances if one can have an instrument or a functionto measure a novelty value of a subject matter (e.g. an OS of thecomposition) itself or a novelty measure for the partitions then itwould become practical to spot the novel information and/or thepartitions of the composition carrying novel information in the contextof that compositions or a set of compositions or generally a body ofknowledge (BOK) as we defined before.

However the degree or value of novelty should be somehow measured inorder to identify the part or partitions of the novelty and evaluatetheir value in terms of the significance of their novelty. In thisdisclosure these measures are called “novelty value significancemeasures” (NVSM) which can be categorized in different types and we,herein, define and show the methods of evaluating them for ontologicalsubjects of a composition or a BOK.

In view of that, the first step is to define what constitute a noveltyin the context of a BOK and identify different aspects that there isinto a novelty investigation.

There could be envisioned several situations in which a novelty canoccur that is of value in the investigation process. The detection andevaluation of novelty values can be important to either a knowledgeconsumer or to be used in other applications, processes, and or othercomputer implemented client programs.

Accordingly, in the present invention we explain few exemplary instancesof novelty, having significance value, to be investigated in moredetails to demonstrate another investigation method of compositionsaccording to novelty significance aspect/s.

II-V-I Relational Novelty

Novelty is an attribute that is related to newness, surprising factors,entropy, not being well known, not seen before, and unpredictability.However this attributes depends very much on the context and inrelations to other ontological subjects of the compositions. Forinstance something which is new in one domain or context might be anobvious thing in another domain. Or something that is new now, it mightbecome vey well known fact after sometimes. For instance, in newsaggregation novelty of the news is very much related to the time of thenews being broken and how many other news agencies have published thesame news story. Therefore the novelty should be measured in relation tothe context, time, and other partitions of the compositions. However, welook for novelty or novelties in the given composition for investigationand since we can treat time and/or a time stamp as an OS, our method ofinvestigation, therefore, would also work for time-related compositionssuch as news, as well.

Generally, therefore, a valuable novelty occurrence is relational (i.e.more than one OS is participated where the novelty occurs) which shouldbe investigated in the context of a composition. For instance in thecontext of a body of knowledge (BOK) there could be found many known oranticipated facts in regards to the subject matter/s of the BOK butthere could be some partitions, e.g. statements, that are less known andcan be considered as novel.

In this subsection therefore, to identify relative or relational noveltyin regards to a topic or one or more OSs, several important noveltyoccurrence situations are envisioned and exemplified in the followings.

One of the situations is a novel relationship between two or more OSs inwhich case there could yet be envisioned at least two notable andimportant situations.

In one situation of novel relationship between two or more OSs, forexample, a type of “relational novelty value significance measure” canbe assigned to spot a novel or less known relationship between twoimportant OSs. In this case the relational novel value should be highbecause the two significant OSs are less seen with each other in a partor partitions of a composition or a BOK. Therefore the desired“relational novel significance measure” should be proportional to thevalue significances of each of the OSs and be inversely proportional totheir “association strength bond”.

Accordingly, one exemplary and simple measure of “relational novel valuesignificance” between two of the OS of order k, say OS_(i) ^(k) andOS_(j) ^(k), can be given by:

$\begin{matrix}{{{{rnvsm\_}1_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto {vsm}_{i}^{k|l}},{vsm}_{j}^{k|l},\frac{1}{{com}_{ij}^{k|l}}} & (39)\end{matrix}$

wherein the rnvsm_1_(i→j) ^(k|l) stands for type one “relational noveltyvalue significance measure” of OS_(i) ^(k) to the OS_(j) ^(k). Thismeasure can be used to hunt for those partitions that contain two ormore significant OSs expressing less known relationship. Therefore thismeasure will give a high value to the pair of the OSs, that areintrinsically significant, and more likely the expressed relationship tobe credible and significant yet their relationship with each other is ofnovelty in the context of the BOK.

Another situation of novel relationship between two or more OSs, is atype of novelty between two OSs in which the novelty reveals less knowninformation about one important OS of the interest (e.g. a targetkeyword, a high value significance subject of a BOK, etc.), regardlessthe significance of the other OSs. In this instance, the intrinsic valueof the target OS, e.g. an intrinsic vsm, should be a significance factorfor measuring and putting a value on the novelty. Also in terms of howto spot a novelty in relation to a significant target OS then the lessknown associations can be a guide to find the novel part or partitionsor statement of a relationship between a significant OS with other OSsof the composition.

Therefore, another type of “relational novelty value significancemeasure” can be defined as:

$\begin{matrix}{{{rnvsm\_}2_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto {{vsm}_{j}^{k|l} \cdot \frac{1}{{com}_{ij}^{k|l}}}} & (40)\end{matrix}$

wherein the rnvsm_2_(i→j) ^(k|l) stand for the second type “relationalnovelty value significance measure” OS_(i) ^(k) to the OS_(j) ^(k). Thismeasure put a high relational novelty value on the pairs that at leastone of them, e.g. the target OS, have a high intrinsic value (i.e thevsm of the OS_(j) ^(k)) while the other ones are the ones that had thelowest co-occurrences with the target OS. This measure can be used tospot the partitions that are novel and significant but perhaps theexpressed relationship, between the two OSs, by the partition, is lesscredible.

Moreover there could be considered further notable situations, when twoor more of OSs of the composition have participated in a partition, toconvey a novel knowledge or information.

Accordingly, for example, another type of relational novelty can occurbetween a less significant OS and a high significance target OS. In thiscase this type of novelty value should be proportional to the valuesignificance of the second OS, e.g. a target OS, and be inverselyproportional to the value significance of the less significant OS andalso be inversely proportional to their co-occurrences so that:

$\begin{matrix}{{{{rnvsm\_}3_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto {vsm}_{k}^{k|l}},{1/{vsm}_{i}^{k|l}},\frac{1}{{com}_{ij}^{k|l}}} & (41)\end{matrix}$

wherein the rnvsm_3_(i→j) ^(k|l) stand for the third type of “relationalnovelty value significance measure” OS_(i) ^(k) to the OS_(j) ^(k). Thismeasure can be used to spot highly novel but perhaps even less crediblepartitions of the BOK than what is found by the rnvsm_2_(i→j) ^(k|l).

And yet another type of novelty can occur between two less significantOSs. In this case the significance and relational novelty value shouldbe inversely proportional to the significances, i.e. VSMs, of each ofthe OSs and also proportional to their co-occurrences so that:

rnvsm_4_(i→j) ^(k|l)(OS_(i) ^(k),OS_(j) ^(k))∝1/vsm_(j) ^(k|l),1/vsm_(i)^(k|l),com_(ij) ^(k|l)  (42)

wherein the rnvsm_4_(i→j) ^(k|l) stands for the forth type of“relational novelty value significance measure” OS_(i) ^(k) to theOS_(j) ^(k). This measure can be used to spot a highly novelrelationship between two less known OSs but with some credibility. Thismeasure can be used to spot the rare partitions that might be irrelevantto the context of the BOK but is important to be looked at.

And yet there could be another notable situation and measure ofrelational novelty as:

$\begin{matrix}{{{{rnvsm\_}5_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto {1/{vsm}_{j}^{k|l}}},{1/{vsm}_{i}^{k|l}},\frac{1}{{com}_{ij}^{k|l}}} & (43)\end{matrix}$

wherein the rnvsm_5_(i→j) ^(k|l) stands for the fifth type of“relational novelty value significance measure” OS_(i) ^(k) to theOS_(j) ^(k). This measure can be used to spot a highly novelrelationship between two less known OSs but with even less credibilitythan rnvsm_4_(i→j) ^(k|l). This measure can be used to spot the noiselike partitions that might be irrelevant to the context of the BOK butmight be essential to be looked at such as crime investigation orfinancial analysis, fraud detections and the like. This measure also canbe used to filter out the irrelevant or noisy part of the composition,or be used in data compression, image compression and the like.

In another notable instance a measure of relational novelty value can bedefined based on their association strengths to each other as:

rnvsm_6_(i→j) ^(k|l)(OS_(i) ^(k),OS_(j) ^(k))∝asm_1_(i→j)^(k|l)/asm_1_(i→j) ^(k|l)  (44)

wherein the rnvsm_6_(i→j) ^(k|l) stands for the sixth type of“relational novelty value significance measure” OS_(i) ^(k) to theOS_(j) ^(k). This measure of novelty amplifies the asymmetry of theassociation strength value between the two OSs and therefore serves as ameasure of anomaly and novelty, both too large and too small a value forthis measure can point to a novelty situation. However, to have asymmetric rnvsm using asm one might consider the following measure:

$\begin{matrix}{{{rnvsm\_}7_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto \left( {\frac{{asm}_{i->j}^{k|l}}{{asm}_{j->i}^{k|l}} + \frac{{asm}_{j->i}^{k|l}}{{asm}_{i->j}^{k|l}}} \right)} & (45)\end{matrix}$

wherein the rnvsm_7_(i→j) ^(k|l) stands for the seventh type of“relational novelty value significance measure” OS_(i) ^(k) to theOS_(j) ^(k). This measure is particularly good to spot any symmetrickind of novelty or anomaly between OS_(i) ^(k) to the OS_(j) ^(k). Whenthe value of this measure is large then there is a novelty situation tolook at between OS_(i) ^(k) to the OS_(j) ^(k).

It can be noted that the some of the exemplary rnvsm_x_(i→j) ^(k|l),(x=1, 2, 3 . . . ) are generally symmetric and both sided whereas thesome other rnvsm_x_(i→j) ^(k|l) are asymmetric.

Once is noted that the co-occurrence is one of the measures andindications of the associations between a pair of OS then thernvsm_x^(k|l) (x=1, 2, . . . ) can further be generalized as a functionof individual values significances of the OSs and their associationstrength measures. Therefore in general the “relational novel valuesignificance measures” can be defined and calculated in the general formof:

rnvsm_x _(i→j) ^(k|l)(OS_(i) ^(k),OS_(j) ^(k))=g ₂(vsm_(i)^(k|l),vsm_(j) ^(k|l),vsm_(i→j) ^(k|l),vsm_(j→i) ^(k|l)), . . . i,j=1,2,. . . N,x=1,2, . . .  (46)

wherein g₂ is a predefined or predetermined function.

When there are multiple OSs of interest the pair-wise valuesignificances can be used in combination and perhaps with various weightto achieve the same filtering effect for a set of OSs. For instance

rnvsm_(q→i,j,p) ^(k|l)(OS_(i) ^(k),OS_(j) ^(k),OS_(p)^(k))=α₁·rnvsm_x1^(k|l)(OS_(q) ^(k),OS_(i)^(k))+α₂·rnvsm_x2^(k|l)(OS_(q) ^(k),OS_(j)^(k))+α₃·rnvsm_x3^(k|l)(OS_(q) ^(k),OS_(p) ^(k)) and q=1,2 . . . N  (47)

wherein α₁, α₂, and α₃ are predetermined weighting functions such asα₁(OS_(i) ^(k))=1/FO(OS_(i) ^(k)) or α₁(OS_(i) ^(k))=log 2(iop(OS_(i)^(k))) etc. or constants and/or normalization factors, and x₁, x₂ and x₃are indications of the type of the rnvsm (e.g. Eq. 39-45) and “OS_(p)^(k)” is the indication of one or more combination of the first OS tothe particular target OS. Moreover, Eq. 47 in just one of the notablesituations of novelty occurrence and in another instance it might becomemore useful to multiply the pair-wise rnvsm_x^(k|l) to each other.

All these relationships (i.e. Eq. 39-46) can be written in a matrix formto, once executed numerically, have all combinations of relationsbetween two or more of the OS^(k) pre-calculated and handy.

Again by operating these specialty defined “value significance measures”on the PM one can obtain the respective type of value for the partitionsof the compositions, e.g. OSs of order l or OS^(l), by:

rnvsm_x _(i) _(l→) _(j) _(k) ^(l→k|kl)=(pm_(i) _(k) _(i) _(l)^(kl))^(T)×rnvsm_x _(i) _(k→) _(j) _(k) ^(k|l) . . . i _(k) ,j _(k)=1,2,. . . N and i _(l)=1,2, . . . M  (48)

Or in the matrix form as:

RNVSM_x ^(l→k|kl)=(PM^(kl))^(T)×RNVSM_x ^(k|l) i _(l)=1,2, . . . M and j_(k)=1,2, . . . N  (49)

wherein the “T” shows the transposition matrix operation and theRNVSM_x^(l→k|kl) is the type x (x=1, 2, . . . ) “relational noveltyvalue significance measure” of the partitions or OSs of order 1 to theOSs of the order k. It is noticed that RNVSM_x^(l→k|kl) is a M×N matrixindicating the type x (x=1, 2, . . . ) “relative novel valuesignificance measure” of OS_(i) ^(l) (e.g. a sentence and i=1, 2, . . .M) to a OS_(j) ^(k) (e.g. a word and j=1, 2, . . . N) and RNVSM_x^(k|l)is a N×N matrix indicating the type x (x=1, 2, . . . ) “relational novelvalue significance measure” of OS^(k) with OS^(k).

In a similar fashion to the previous subsection, there could becalculated a novelty type relationships between the OSs of order l sothat to show how each pair of the partitions are related in terms of thesignificance of the relational novelty to each other as:

RNVSM_x ^(l→l|kl)=RNVSM_x ^(l→k|kl)×RNVSM_x ^(l→k|kl)  (50)

wherein RNVSM_x^(l→l|kl) stands for the “relational novelty valuesignificance measure” of type x between the OSs of the order l, which isa M×M matrix. This measure and the data of such matrix can be used tofind a novel partition, exhibiting a predetermined range of “relationalnovelty value”, for a given partition. Also these measures can becombined with other measures to obtain the desired parts of thecompositions that one is looking for (e.g. in response to a query or aquestion).

II-V-II the Association Type Novelty

Many associations are hidden that when is revealed is obviously a caseof novelty existence or occurrence. For instance when two OSs havelittle direct associations but their association spectrum is highlycorrelated then there could be a novelty of high value revealed forfurther investigation. In these instances a measure to hunt for thesetypes of novelty association can be given by:

$\begin{matrix}{{{{anvsm\_}1_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto \frac{\left( {{asm\_ x1}_{p->i}^{k|l} \cdot {asm\_ x2}_{p->j}^{k|l}} \right)}{{asm\_ x}\; 3_{i->j}^{k|l}}},{p = 1},2,{\ldots \mspace{14mu} N}} & (51)\end{matrix}$

wherein anvsm_1^(k|l) is indicative of the first type “associationnovelty value significance measure”, the “.” shows the inner product orscalar multiplication of the asm_x1_(p→i) ^(k|l) and asm_x2_(p→j) ^(k|l)vectors. The indices of x1, x2, x3 (=1, 2, . . . etc) are usually equaland can refer, for instance, to the first or the second type associationstrength measure (given by Eq. 16, and/or 17-26).

This measure of novelty gives a high value to the relational novelty ofthose pairs that exhibit strong hidden association correlation but theyare not explicitly strongly bonded. This measure is particularly usefulfor detecting hidden relationships between two OSs of interest, i.e.OS_(i) ^(k) and OS_(j) ^(k) and can be used to spot the cases worthy offurther research and investigation (e.g. in scientific discovery,medical, crime investigation, genetics, market research and financialanalysis etc.).

Although anvsm_1^(k|l) is also one of the “relational novelty valuesignificance measures” but in here it is preferred to be given a moredistinct name as “association novelty value significance measure”(ANVSM) in order to have a distinct category for this kind of “valuesignificance measure” in general.

To further amplify the significance of the novelty of anvsm_1^(k|l) onecan further incorporate the intrinsic value significance of one or bothof the value significances of the OS_(i) ^(k) and OS_(j) ^(k) as, forexample, the following:

$\begin{matrix}{{{{anvsm\_}2_{i->j}^{k|l}\left( {{OS}_{i}^{k},{OS}_{j}^{k}} \right)} \propto \frac{\left( {{vsm\_ y1}_{i}^{k|l} \cdot {vsm\_ y2}_{j}^{k|l}} \right) \times \left( {{asm\_ x1}_{p->i}^{k|l} \cdot {asm\_ x2}_{p->j}^{k|l}} \right)}{{asm\_ x}\; 3_{i->j}^{k|l}}},\mspace{20mu} {p = 1},2,{\ldots \mspace{14mu} N}} & (52)\end{matrix}$

wherein y1 and y2 indicates the types and numbers of the “valuesignificance measure” used in this formula.

The proportionality factor can be adjusted to account for normalizationof the vectors when desired.

Eq. 51 can be re written in matrix form in general terms which is moreuseful as:

ANVSM_1_^(k|l)=[(ASM_x1^(k|l))^(T)×ASM_x2^(k|l)]·/ASM_x3^(k|l)  (53)

wherein “x” shows the matrix multiplication operator and “·/” shows theelement-wise division. Usually, in the preferred exemplary embodiment,in the Eq. 53 the ASM_x^(k|l) are column or row normalized.

As can be seen Eq. 51, 52 and 53 are generally the exemplary cases ofthe general form of:

anvsm_x _(i→j) ^(k|l)(OS_(i) ^(k),OS_(j) ^(k))=g ₃(vsm_y1_(i)^(k|l)·vsm_y2_(j) ^(k|l),asm_x1_(p→i) ^(k|l)·asm_x2_(p→j)^(k|l),asm_x3_(i→j) ^(k|l),asm_x4_(i→j) ^(k|l)), . . . p,i,j=1,2, . . .N,  (54)

wherein g₃ is predetermined or predefined function and y1, y2, x1 . . .x4 etc refer to the selected type of the respective kind and type of the“value significance measure”.

Numerous other forms of “value significance measures” using one or moreof the introduced “value significance measures” and the concept behindthem can be devised, depends on the applications, which are not furtherlisted here, and in light of the teachings of the present inventionbecome obvious to those skilled in the art.

II-V-III the Intrinsic Novelty

Another important situation of novelty occurrence would be to spot andfind the novel OSs and the partitions of the composition regardless oftheir relationship and just for being intrinsically novel in the contextof the composition or convey novelty wherever they appear in thecomposition or the BOK.

In this case we assign an intrinsic “novelty value significance measure”(NVSM) to each desired OS and then use the NVSM to weight the intrinsicnovelty value of other partitions.

The first measure of novelty of course can be derived and defined basedon the independent probability of occurrence so that:

nvsm_1_(i) ^(k|l) =h ₁(iop_(i) ^(k|l)),i=1,2, . . . N  (55)

wherein h₁ is a predetermined function such as h_(i) (x) be a linerfunction (e.g. ax+b), power of x (e.g. x³ or x^(0.53)), logarithmic(e.g. a/log 2(x)), 1/x, etc wherein a or b might be scalar constant or avector.

Usually the term “novelty” implies that it should be inverselyproportional to the popularity or frequency of occurrence or independentprobability of occurrence and therefore nvsm_1_(i) ^(k|l) is usuallymore justified when the choice of h₁ is such that it decreases as theiop_(i) increases. For instance one good candidate for defining andcalculating a “novelty value significance measure” as a vector is:

nvsm_1_1_(i) ^(k|l) =c/iop_(i) ^(k|l)),i=1,2, . . . N  (56)

wherein c might be a scalar or a constant vector. In another instance itmight be defined as:

nvsm_1_2_(i) ^(k|l) =c/log_(b)(iop_(i) ^(k|l)),i=1,2, . . . N  (57)

or in another instance:

nvsm_1_3_(i) ^(k|l) =c·log_(b)(1/iop_(i) ^(k|l))=−c·log_(b)(1/iop_(i)^(k|l)),i=1,2, . . . N  (58)

or yet in another instance:

$\begin{matrix}{{{nvsm\_}1\_ 4_{i}^{k|l}} = {{- c} \cdot \frac{\log_{b}\left( {iop}_{i}^{k|l} \right)}{{iop}_{i}^{k|l}}}} & (59)\end{matrix}$

wherein b is a constant and c could be constant or a vector. For examplec can be an auxiliary vector that when multiplies to other vectors itsuppresses or dampen the value of particular OSs of the compositionssuch as the generic words in a textual composition.

Accordingly, by the same manner, there could be defined various “novelvalue significance measures” if the justification is properly done. Forinstance with combination of one or more of the nvsm_x_(i) ^(k|l) orother variables there could be defined more sensible and useful noveltyvalue significances. As can be seen in Eq. 59 the nvsm_1_4_(i) ^(k|l) isin fact obtained by multiplication of the nvsm_1_1_(i) ^(k|l) andnvsm_1_3_(i) ^(k|l).

In another aspect the novelty is observed in relation or combinationwith other OSs since novelty could occurs in a context and therefore inrelation to other ontological subjects. The stand alone or the intrinsic“novelty value significance value” in this case is defined as sum of thenovelty that an OS will have with a desired number of other OSs.

These measures of novelty are intrinsic since it adds up all thepair-wise novelty values for each OS^(k) so that a NVSM type 2 can bedefined as:

NVSM_2^(k|l)(OS_(i) ^(k))=cΣ _(j)rnvsm_x _(i→j) ^(k|l)(OS_(i)^(k),OS_(j) ^(k))  (60)

wherein the pair-wise novelty measures are summed over the column (i.e.the j subscript).

Similarly another type of intrinsic novelty value significance measurecan be defined as:

NVSM_3^(k|l)(OS_(j) ^(k))=cΣ _(i)rnvsm_x _(i→j) ^(k|l)(OS_(i)^(k),OS_(j) ^(k))  (61)

wherein the summation is over the rows (i.e. the i subscript).

The same can be calculated using anvsm_x_(i→j) ^(k|l) as:

NVSM_4^(k|l)(OS_(i) ^(k))=cΣ _(j)anvsm_x _(i→j) ^(k|l)(OS_(i)^(k),OS_(j) ^(k))  (62)

and also:

NVSM_5^(k|l)(OS_(j) ^(k))=cΣ _(i)anvsm_x _(i→j) ^(k|l)(OS_(i)^(k),OS_(j) ^(k))  (63)

Or in a general form any combination of them can still serve as anintrinsic measure of novelty of the OSs of the composition as:

NVSM_x ^(k|l)(OS_(i) ^(k))=h(NVSM_1^(k|l),NVSM_2^(k|l), . . . NVSM_y^(k|l))  (64)

wherein h is predetermined function and y is the type and number of theparticular NVSM^(k|l) used into building other types of NVSM_x^(k|l).

These various novelty value measures can find and have many applicationsin variety of applications and compositions which can be employed toinvestigate such composition to find and investigate the parts orpartitions of novelty values. For instance they can be employed fortextual composition processing such as question answering,summarization, knowledge discovery, as well as other kind ofcompositions like detecting novel and valuable parts in a genetic codestrings, finding and filtering the junk DNA, as well as othercompositions such as image and video compositions and signal processingsuch as edge detection, compression, deformations, re-composition toname a few.

II-VI-Transformation and Alteration of Data Objects

The parameters, vectors, and matrices of the present invention aretransformation of the information hidden in the participation matrixwhich can be used for different applications with ease, convenience andefficiency to investigate various aspects of interests in the BOK suchas extracting the most significant parts or partitions, finding thehighly associated concepts or parts and partition, finding the novelpart/s or partition/s of the BOK, finding the best piece of informativepart of the composition, clustering and categorization of the partitionsof the composition or the BOK, ranking and scoring partitions of acomposition based on their relatedness to a subject matter (e.g. aquery), excluding one or more partitions or OSs of the BOK orsuppressing their role in the analysis, and numerous other application.

Moreover the mathematical objects and data arrays can be easilytransformed to other forms, filtered out the desired part or segment ofa matrix, amplify or suppress the role of one or more of the OSs of thecomposition and/or their values being altered numerically withoutneeding to manipulate the input composition string or file. For instancein many of the above calculations it will be more useful to have thematrices or vectors being normalized in order to make the comparisonsmore meaningful in the context of the BOK. Accordingly one or more ofsuch mathematical objects and data arrays (vectors, matrices etc.) canand might be desired to become column or row normalized or further beingmultiplied by other matrices or vectors as a mask or filter etc.

Moreover all these matrices (e.g. such as PM, COM, ASM/s, RASM, RVSMsNVSM, RNVSMs etc.) can be regarded as an adjacency matrix for acorresponding graph wherein the matrix carry the data of theconnectivity between the nodes or objects of the graph. Therefore, fromthese connectivity matrixes one can proceed to calculate a correspondingeigenvalue equation/s in order to estimate and calculate other types ofdesirable value significance measure or in general any type of valuesignificance. These measures of value calculated from the correspondingeigenvalue equations of the matrices are generally indication ofintrinsic significance values of the OSs. For instance in thenon-provisional U.S. patent applications of Ser. Nos. 12/547,879,12/755,415 and 12/939,112 one or more of these matrices have been usedto calculate the significance values of the OSs of the composition basedon their centralities of the corresponding node in the graph that couldbe represented by that matrix. The centrality value can be, forinstance, be the values of largest eigen vector of the eigen value asdescribed in the application Ser. Nos. 12/547,879, 12/755,415 and12/939,112 which are incorporated here as references.

II-VI-I-Special Case Coveyers

In many cases one wants to deliberately amplify and/or dampen orsuppress one or more of the values of OS of the BOK in order to achievethe right functionality out of the analysis and investigation. Thereforethere could be per-built or pre-determined VSM values (e.g vectors) thatcan be used when it is desired to alter and influence the significancevalues of one or more of the OSs of the compositions. For instance thesevectors or filter can be designed in such a way to amplify thesignificances of proper sentences of compositions written in aparticular natural language such as English. For example, in anotherinstance, the objective can be to give significance to particular typesof partitions of the composition having of particular feature/s,attribute/s, or form/s. For instance when one like to hunt thepartitions containing connecting or the concluding remarks then one mayconstruct a vector that assigns a low significance value to every OSexcept those selected OS (e.g. words or phrases such as “therefore”, “asa result”, “hence”, “consequently”, “so that” . . . etc.). n anotherinstance, one might have list of OSs that it is not desirable toparticipate in the calculation (e.g. stop words) one can provide avector over the range of OSs having a value of one expect for thoseselected OS that must be omitted from the calculation.

These pre-assigned vectors are called “special cases conveyers” hereinor “significance value conveyer vectors” as shown in FIG. 6c , that canbe used solely or in combinations with other VSM value vectors to obtainthe desired functionality from the investigation. These conveyers areassigned and used based upon the goal of investigation. The specialconveyers can be designed and altered for various stage of the processand can be used in different stages of calculations and processes.

II-VI-II-PM Transformation

In accordance with another aspect of the methods of investigation of thecompositions of ontological subject of the present invention, theparticipation matrix can, for instance, routinely being transformed toother types of objects or participation matrices by operating one ormore vector or matrices on the PM. For example one can multiply the PMby a diagonal matrix (M by M) from the right side whose diagonal valuesare the reciprocal of the number of constituent OSs of order k in thepartitions or the higher order OS of order l. The “resulting PM” matrixwill become a column normalized PM and values of the entries will becomethe weighted participation factor. For instance from a binary PM one canget to partial PM in which if a word has participated in a sentence with5 words then its participation entry in the PM would be ⅕ and if thesame word has participated in a sentence with 10 words its participationentry would be 1/10 and so on. In another instance, in a similarsituation, it become desirable to have a “resulting PM” with columngeometrical unitary (i.e. the length of the column become one), in thiscase therefore the elements of the diagonal matrix are the inverse ofthe square-root of the sum of the square of the individual elements ofthe original respective PM column (or row).

As another instance of transformation, moreover, the PM matrix can bemultiplied from the left side by a diagonal matrix (N by N) whoseentries are a vector that will put a value on the OS of the order k sothat their participation weight will be altered. For instance if thediagonal of the left matrix is one except for some particular words(such as the generic words of a natural language) for which thecorresponding entries are suppressed (e.g. replaced with 0.1) then therole of those particular words (e.g. the generic words) in thecomputations will be suppressed as well, without having to manipulatethe original string of the compositions in order to achieve the samegoal of suppressing the role of generic words.

As another instance of transformation and alteration, one or moreauxiliary vectors (i.e. filters) can be built to dampen the significanceof particular OSs of the composition by multiplying those vectors on theresulting vector objects such as one or more of the different types andnumber of the “value significance measures” vectors or matrices.

Moreover the method/s can conveniently be used for compositions ofdifferent nature such as data file compositions, e.g. audio or videosignals, DNA string investigation, textual strings and text files,corporate reports, corporate databases, etc. For instance theinvestigation method disclosed herein can be readily used to investigateimage and video files, such as spotting a novelty in an image or pictureor video, edge detection in an image, feature/s extraction, compressionof image and video signals, and manipulating the image etc. Thedisclosed methods of the present invention can readily be applied inapplications such as, artificial intelligence, neural network trainingand learning, network training, machine learning, computer conversation,approximate reasoning, as well as computer vision, robotic vision,object tracking etc.

Numerous other forms of “value significance measures” using one or moreof the introduced value significance measures and the concept behindthem can be devised and synthesized accordingly, depends on theapplication, that are not further listed here but in light of theteachings of the present invention become obvious to those skilled inthe art.

The disclosed frame work along with the algorithms and methods enablesthe people in various disciplines, such as artificial intelligence,robotics, information retrieval, search engines, knowledge discovery,genomics and computational genomics, signal and image processing,information and data processing, encryption and compression, businessintelligence, decision support systems, financial analysis, marketanalysis, public relation analysis, and generally any field of scienceand technology to use the disclosed method/s of the investigation of thecompositions of ontological subjects and the bodies of knowledge toarrive the desired form of information and knowledge desired with ease,efficiency, and accuracy.

Furthermore, as pointed out before, those skilled in the art can store,process or represent the information of the data objects of the presentapplication (e.g. list of ontological subjects of various order, list ofsubject matters, participation matrix/ex, association strengthmatrix/ex, and various types of associational, relational, novel,matrices, co-occurrence matrix, participation matrices, and other dataobjects introduced herein) or other data objects as introduced anddisclosed in the incorporated references (e.g. association valuespectrums, ontological subject map, ontological subject index, list ofauthors, and the like and/or the functions and their values, associationvalues, counts, co-occurrences of ontological subjects, vectors ormatrix, list or otherwise, and the like etc.) of the present inventionin/with different or equivalent data structures, data arrays or formswithout any particular restriction.

For example the PMs, ASMs, OSM or co-occurrences of the ontologicalsubjects etc. can be represented by a matrix, sparse matrix, table,database rows, dictionaries and the like which can be stored in variousforms of data structures. For instance each layer of the a Pm, ASM, OSM,RNVSM, NVSM, and the like or the ontological subject index, or knowledgedatabase/s can be represented and/or stored in one or more datastructures such as one or more dictionaries, one or more cell arrays,one or more row/columns of an SQL database, one or more filing systems,one or more lists or lists in lists, hash tables, tuples, string format,zip format, sequences, sets, counters, or any combined form of one ormore data structure, or any other convenient objects of any computerprogramming languages such as Python, C, Perl, Java, JavaScript etc.Such practical implementation strategies can be devised by variouspeople in different ways.

The detailed description, herein, therefore describes exemplary way(s)of implementing the methods and the system of the present invention,employing the disclosed concepts. They should not be interpreted as theonly way of formulating the disclosed concepts, algorithms, and theintroducing mathematical or computer implementable objects, measures,parameters, and variables into the corresponding physical apparatusesand systems comprising data/information processing devices and/or units,storage device and/or computer readable storage media, data input/outputdevices and/or units, and/or data communication/network devices and/orunits, etc.

The processing units or data processing devices (e.g. CPUs) must be ableto handle various collections of data. Therefore the computing units toimplement the system have compound processing speed equivalent of onethousand million or larger than one thousand million instructions persecond and a collective memory, or storage devices (e.g. RAM), that isable to store large enough chunks of data to enable the system to carryout the task and decrease the processing time significantly compared toa single generic personal computer available at the time of the presentdisclosure.”

II-VII—the Exemplary Implementation Methods and the Exemplary Systemsand Services

This section describes few exemplary systems that can be constructed inorder to demonstrate the enabling benefits of the deployment of thedisclosed method/s of investigation of compositions of ontologicalsubjects in various challenging applications and importantfunctionalities.

As was described throughout the description the goal of theinvestigation is to produce a useful data, information, and knowledgefrom a given or accessed composition/s, according to at least one aspectof significance or the goal/s of the investigation.

The result of the investigation can be represented in various forms andpresentation style and various devices of modern information technology(private or public cloud computing, wired or wireless connections,etc.). The interaction between a client and an investigator, employingone or more of the disclosed algorithms, can be facilitated throughvarious forms of data network accessibility to an investigator throughvarious interfaces such as web interfaces, or data transferringfacilities. The result of the investigation can be displayed or providedin various forms such as interactive page/device environment, graphs,reports, charts, summaries, maps, interactive navigation maps, email,image, video compositions, voice or vocal compositions, different naturecomposition such as transformation of a textual composition to visual orvice versa, encoded data, decoded data, data files, etc.

For instance a goal of investigation can be to finding out the OSs ofthe composition scoring significant enough novelty value in the contextof the given BOK or an assembled BOK wherein the OSs of the compositioncan be words, phrases, sentences, paragraphs, lines, document or thelike for the BOK under investigation.

Another exemplary goal of investigation can be to get a summary of thecredible statements from a BOK or to modify a part or partitions of acomposition (e.g. a document, an image, a video clip etc.). Or anotherinstance of investigation can be to obtain a map of relations betweenthe most significant parts or partitions of the BOK. For instance apatent attorney, inventor, or an examiner can use the disclosed methodto plan his/her claim drafting by investigation the applicationdisclosure and get the most valuable or novel part of the disclosure todraft the claims. Or to get the map of relationships between thecomponents (i.e. the ontological subjects) of the disclosure in order todraft a summary, an abstract, an argument, one or more claims,litigation, etc. Or the method can be used for examining the applicationin comparison to one or more collection of one or more patentapplication disclosures.

In another instance an intelligent being (e.g. a software bot/robot ahumanoid, a machine, or an appliances) can use the system and methodsinternally or by connecting/communicating to a provider of such servicesto become enabled to interact intelligently with human (e.g. conversingand doing tasks, or entertaining, or assisting in knowledge discoveretc.). And many numerous other examples that could be using one or moreof the tools, measures and method/s given in this disclosure to getinformation and finding/composing the knowledge that is being desired orseek after.

Referring now to the accompanying drawings in here, few exemplaryembodiments of the methods, the systems and the applications are furtherillustrated and explained in order to demonstrate the deployment of theteaching of the present invention.

Referring to FIG. 1 here, it depicts one general flow process and thesystem that can provide one or more exemplary investigation's result, asservices, utilizing the algorithms and the methods of the presentinvention. As shown in the diagram, following the above formulations andmethods of building the required variables or the mathematical or dataobjects (e.g. the matrices and the vectors values etc) and building thevarious filter, one can design, synthesize, and compose an outputaccording to her/his/it's need or goal of investigation or informationalrequirements and for an input composition. For example if oneapplications calls for getting the most credible and valuable partitionsof an input compositions then she/he/it must chose (or select through aninterface) the corresponding filter (i.e. the suitable XY_VSM/s andalgorithm/s) for which to obtain such a credible glance or summary ofthe composition. Moreover the user or the designer of such system andservice can synthesize the suitable filter, using the tools, measuresand methods of the present invention to provide the desired response,output or the service.

Alternatively, in another instance, if one is looking only to get thenovel parts of the input composition then that can also be readily donefollowing the teaching and computational process of the above to get thenovel parts or partitions of the composition using the one or more ofthe novelty value significance measures.

Turning to FIG. 1 again, as seen in the FIG. 1, the input composition isused to build or generate the one or more participation matrices whilethe ontological subjects of different orders are grouped, listed, andkept in the short term or more permanent storage media. The actual OSsor the partitions usually are used at the end of the processing andcalculations of the desired quantity or quantities, when they arefetched again based on their corresponding value for one or moremeasures of the values introduced in previous sections. Accordinglyafter having the PM/s the system will calculate the desired mathematicalobjects such as COM, ASM/s, the desired VSM/s, one or more RASM ifneeded for the desired service, one or more RVSM/s if needed for theservice, one or more of NVSM/s, or RNVSM/s or ANVSM/s if desired and soon.

These data objects (e.g. matrix/es or vector/s) are used to synthesizethe required filter to provide the desired functionality once itoperated on the PM. After operating the filter on the PM, the output isfurther investigated for selection of suitable OSs of the compositionfor further processing or re-composing or presentation. The output canbe presented in predetermined form/s or format, such as a file,displaying on a web-interface or an interactive web-interface, encodeddata in a particular format for using by another system or softwareagent, sending by email, being displayed in a mobile device, projectorand the like over a network, or sent to a client over the internet andthe like.

For instance if the desired mode of operation is to find out the novelpartitions of the composition exhibiting enough novelty value whilehaving enough significance then the corresponding filter will use theRNVSM of the Eq. 39 for finding, scoring and consequently selection ofthe suitable partitions for this requested service.

In another word after the composition data are transformed ortransported into participation matrix/matrices then we only deal withnumerical calculations that will determine the value of the members ofthe listed OSs and (based on their index in the list or based on theirrow or column number in the participation matrix) once the value for thecorresponding measure was calculated then those OSs that exhibited thedesirable value or range of values are selected by the selector or acomposer that provide the output data or content, e.g. as service,according to predetermined formats for that service.

In references to FIG. 2 now, it involves the conceptualization of theassociation strength measure/s. As exemplified several times along thedisclosure the concept and values of “association strength measure/s”plays an important role in investigation of the composition ofontological subjects as well as providing the data that is valuableitself. That is, knowing the association strength of OSs to each otheris important and can be used to build many other applications especiallyin artificial intelligence applications.

Accordingly, in FIG. 2 here, it is shown one general form ofconceptualizing and defining the association strength measures andconsequently calculating the association strength values for thosemeasures. As seen in this exemplary embodiment the association strengthof the OSs of order k that have co-occurred in one or more OSs of orderl is given by a function of their number of co-occurrence and thevalue/s respective of one or more of the “value significance measure/s”(e.g independent probability of occurrence). Several exemplified suchassociation strength measure were given by Eq. 16-24. The FIG. 2 wasalso illustrated in some details in the section II-III of thisdisclosure.

Referring to FIG. 3 now, it is to show that any composition ofontological subjects can in principal be represented by a graph which inthis preferred embodiment shown as an asymmetric graph. The exemplifiedgraph is corresponded to one of the exemplary “association strengthmatrix”, i.e. an ASM, as representative of its adjacency matrix. Thenodes represent the desired group of OSs and the edge or arrows show thelink between the associated nodes and the values on the edges arerepresentative of the association strength from one node to theconnected one. This figure is to graphically exemplify and depicts thatcompositions of ontological subjects and a network of ontologicalsubjects can basically be investigated and dealt with in the same manneraccording to the teachings of the present invention.

In FIG. 4, there is shown again another embodiment for the process ofcalculating various value significance measures in more details. As seenthe data of the input composition is transformed to calculablequantities and data from which, employing the above methods andformulations, the desired value significance measures are calculatedand/or are stored in the storage areas for further use or being used byother processes or programs or clients.

In reference to FIG. 5, it became evident that at this stage, and inaccordance with the method, and using one or more of the participationmatrix and/or the consequent matrices one can also evaluate thesignificance of the OSs by building a graph and calculating thecentrality power of each node in the graph by solving the resultanteigen-value equation of adjacency matrix of the graph as explained inpatent application Ser. No. 12/547,879 and the patent application Ser.No. 12/755,415.

FIG. 5 therefore shows the block diagram of one basic exemplaryembodiment in which it demonstrates a method of using the associationstrengths matrix (ASM) to build an “Ontological Subject Map (OSM)” or agraph. The map is not only useful for graphical representation andnavigation of an input body of knowledge but also can be used toevaluate the value significances of the OSs in the graph as explained inthe patent application Ser. No. 12/547,879 entitled “System and Methodof Ontological Subject Mapping for knowledge Processing Applications”filed on Aug. 26, 2009 by the same applicant. Utilization of the ASMintroduced in this application can result in better justifiedOntological Subject Map (OSM) and the resultant calculated significancevalue of the OSs.

The association strength matrix could be regarded as the adjacencymatrix of any graphs such as social graphs or any network of any thing.For instance the graphs can be built representing the relations betweenthe concepts and entities or any other desired set of OSs in a specialarea of science, market, industry or any “body of knowledge”. Therebythe method becomes instrumental at identifying the value significance ofany entity or concept in that body of knowledge and consequently beemployed for building an automatic ontology. The VSM_1, 2, . . . x^(k|l)and other mathematical objects can be very instrumental in knowledgediscovery and research trajectories prioritizations and ontologybuilding by indicating not only the important concepts, entities, parts,or partitions of the body of knowledge but also by showing their mostimportant associations.

Referring to FIG. 6a, 6b, 6c now, they show one graphical representationof the concept of the different values of different “value significancemeasures”. As seen values of different types of value significancemeasures (labeled as XY_VSM wherein XY is used to show the differenttypes of VSM/s) can be shown as a vector in a multidimensional space.Though XY_VSM/s in general are matrices that might also carry therelational value significances but still any row or column (as shown inFIG. 6 a) of them can be shown as discrete vectors in a multidimensionalspace. These discreet vectors can also be treated as discrete signals inwhich they can be further be used for investigation of the compositions.Some types of XY_VSM, that are intrinsic, are vectors (e.g. FIG. 6b )for which they can readily be used to weigh other OSs or the partitionsof the composition. Also shown in FIG. 6c are some of the vectors thatmight be “special conveyer vectors” labeled with “significance conveyervectors” in the FIG. 6c and are usually predefined or predetermined thatcan be used for filtering out and/or dampening or amplifying and/orshaping/synthesizing the VSMs of one or more of the predetermined OSs ofthe composition. FIG. 6c demonstrate that special conveyer vectors orVSM have basically the same characteristics as other XY-VSM except thevalues might have been set in advance.

FIG. 7 shows one way of demonstrating (e.g. schematically) how twoexemplary value significance vectors can be extracted from an exemplary“association strength matrix” (asm) which in this instance are alsoshown to be used to evaluate the associations of OSs of order l (e.g.sentences) to particular OS of order k (e.g. a word or keyword orphrase). Generally FIG. 7 is for further clarification and instantiationof the actual meaning and their use and the way to manipulate and use,deal, and calculate the variables and data or mathematical objects thatwere introduced in the previous sections. However, the disclosedprocesses and methods with the given formulations should be enough forthose of ordinary skilled in the art to enable them to implement,execute, and apply the teachings of the present invention.

An application of the instance demonstration of FIG. 7 is that an OS oforder l, can be selected by the investigator based on its strength ofassociation to one or more OSs of the order k. The calculation and theselection method of OSs of order l can find an important application indocument retrieval, question answering, computer conversation, in whicha suitable answer or output is being south from a knowledge repository(e.g. a given composition) in response to the input query orcomposition. As an example, for showing how to utilize the disclosedmethod/s, an input statement or a query is parsed to its constituent OSsof order k and from the association strength matrix (which might beconstructed from and for said knowledge repository) then the mostlyrelated partitions of the stored composition (i.e. the knowledgerepository) is retrieved in response of an input query which is aconversational statement or a question. For instance, the mostly relatedpartition of the knowledge repository can be the partition (OS of orderl) that has scored the highest average or cumulative association to theconstituent OSs of the input query. The mostly related partition of theknowledge repository might have scored the highest, for example, aftermultiplication of the association strength vectors of the OSs of theinput query in the association strength matrix that have been built fromthe knowledge repository.

Referring to FIG. 8 now, it shows, in schematic, a block diagram of anexemplary system as well as the process of further clarification as howto use the “value significances” data of one or more OSs of particularorder to evaluate and calculate the one or more “value significances” ofOSs of another order using the one or more XY_VSM and one or moreparticipations matrix. The XY in the FIG. 8 is the indication, and canbe replaced with the desired type and number combination, of the desired“value significance measure”. Therefore XY_VSM in FIG. 8 can be replacedwith any of the different types of the “value significance measures”(such as RVSM, NVSM, ARASM, RSVM, etc.). The data objects can be stored,if desired, for later use so that the pre-calculated data and objectsare pre-made and can easily be retrieved for the correspondingcompositions and the desired application. The pre-made stored data canbe used to accelerate and speeding up the process of compositioninvestigation in a system that provide such a service/s to one or moreclients.

Referring to FIG. 9 now it shows an exemplary system, process andapplication of the present invention. FIG. 9 shows an instance ofclustering and ranking, and sorting of a number of webpages fetched fromthe internet for example, by crawling the internet. This is todemonstrate the process of indexing and consequently easily andefficiently finding the relevant information related to a keyword or asubject matter. This is the familiar but very important application andexample of the present invention to be used in search engines. As seenafter crawling a number of webpage or documents from the internet (orfrom any other repository in fact) the pages/documents/compositions areinvestigated so that the associations of the desired part or partitionsof such collections are calculated to other desired OSs of thecollection of the compositions. Now, in such a exemplary search engine,once a client enter a query or a keyword, it would be straightforward tofind the most relevant document, page, or composition to the inputquery, i.e. or a target OS.

Accordingly, as discussed in the previous sections, having one or moreof the “association strength matrix/es” (indicated by XASM) or RVSMsetc., using the disclosed algorithms make it possible to retrieve thedocuments with the highest degrees of relevancy to the input query orthe target OS. This is one of the very important applications andimplication of the disclosed teachings and materials, since, as isexperienced by many users of the commercial search engines; therelevancy of retrieved documents to the input query has been and is amajor challenge in improvement of the search engine performance.However, employing the investigation methods of present invention,through its various measures, make it possible to quickly and reliablyretrieve the most semantically related document/page to the input query.

Furthermore, some special OSs can be selected for which the associationstrength of pages are to be calculated. For instance, special OSs can bethe content words such as nouns or named entities. Nevertheless therewould be no limitation on the selection or choice of the target OS andthey can basically be all possible types of words, or even sentences andhigher orders partitions.

Moreover, through the investigation of crawled pages, either in one stepor in several steps, OSs of high value significance can be identified sothat the whole composition (i.e. the whole collection of the documentsor pages) can be clustered or categorized into bodies of knowledge underone or more target subject matter or head categories (e.g. the highvalue OSs of lower order, such as words or phrases).

The target OSs could usually be the keywords or phrases, or the words orany combinations of the characters, such as dates, special names, etc.However in extreme but useful case the target OSs of such compositioncould be the extracted sentences, phrases, paragraphs, or even a wholedocument and the like.

As seen from the teachings of the present invention then it becomesreadily straightforward to calculate the association and relevancy ofeach part of such a composition (such as the webpages or documents ortheir parts thereof) to each possible target OSs. These data are storedand therefore upon receiving a query (such as a keyword or a question ina natural language form, or in the form of a part of text etc.) thesystem will be able to retrieve the most relevant partitions (e.g. asentence, and/or paragraph, and/or the webpage) and present it to theuser in a predetermined format and order.

Let's exemplify and explain this even in more detail here, when aservice provider system such as a search engine, question answering orcomputer conversing, which comprises or having access to the system ofFIG. 9, receives a query from a user, the system can simply parse theinput query and extract all or some of the words of the input query(i.e. the OSs of order one) then by having calculated the associationsstrength of rasm_x^(1→5|) one can easily calculate the associationstrength of each of the documents (e.g. wep-pages) to the words of theinput query, and eventually the documents which have the overallacceptable association strength with the selected words of the inputquery will be presented to the queries as the most relevant document orcontent.

In another exemplary method of retrieval using this embodiment the mostrelated document or partition to the input query are identified andretrieved or fetched as follow:

-   -   extract the OSs (e.g. words) of the input query,    -   obtain the rasm_x^(1→5|) vector (e.g. the association strength        of a words to each other obtained from the investigation of the        crawled repository of webpages consisting one or more        webpages/documents) for the input words of the query,    -   make a common association strength spectrum or vector for the        input words of the query by, for example, averaging the        rasm_x^(1→5|) vectors or multiplying them to each other,    -   use the common association vector to identify the most related        or associated documents, or sentences to the input query by        multiplying the common association spectrum with the respective        participation matrix (e.g. PM¹⁵ for document retrieval and PM¹²        for question answering or conversation as an example).

Moreover most of calculation can be done in advance and even for eachtarget OSs (though not as a condition but usually the intrinsicallysignificant OSs can be used as possible target) and therefore therecould be assembled for each possible target OS a body of knowledgepre-made and pre categorized and ready for retrieval upon receiving aquery by a system which has access to these data and materials. Thedegree of relevancy of such retrieved pages to the target OSs (e.g. theuser's Queries) is semantically insured and the relevancy of suchretrieved materials far exceeds the quality of the currently availablesearch engines.

More importantly in a similar manner the engine can return for instancethe document or the web-page that composed of the partitions of highnovelty values, either intrinsic or relative, to the target OS/s.Therefore the engine can also filters out and present the documents orwebpages that have most relevancy to the desired “significance aspect”based on the user preferences. So if novelty or credibility orinformation density of a document, in the context of a BOK, is importantfor the user then these services can readily be implemented in light ofthe teachings of the present invention.

Referring to FIG. 10 now, it shows schematically a system of compositioninvestigations that can provide numerous useful data and information toa client or user as a service. Such output or services in principal canbe endless once combined in various modes for different application.However in the FIG. 10 a few of the exemplary and important anddesirable outputs are illustrated. The FIG. 10 illustrates a blockdiagram system composed of an investigator and/or analyzer and/or atransformer and/or a service provider that can receive or access acomposition and provide a plurality of data or content as output. Theinvestigator in fact implement at lease one of the algorithms ofcalculating one of the measures in order to assign a value on the partor partitions of the compositions and based on the assigned valueprocess one or more of the partitions or OSs of the particular order asan output in the form of a service or data. The output could be simplyone or more tags or OS/s that the input composition can be characterizedwith, i.e. significant keywords of the composition. In this instance,the significant keywords or labels are selected based on their valuescorresponding to at least one of the aspectual XY_VSM, i.e. one of thevalue significance measures.

As another example, the output or outcome of the investigator of FIG.10, could be to provide the partitions of the input composition whichhave exhibited intrinsic value significances of above a predeterminedthreshold. Another output could be the novel parts or the OSs of thecompositions that scored a predetermined level of a particular type ofnovelty value significance. Or the output could be the noisy part of acomposition or a detected spam in a collection of compositions etc.

Several other output or services of the system of FIG. 10 are depictedin the FIG. 10 itself which are, in light of the foregoing, selfexplanatory.

Referring to FIG. 11 now, it shows another instance and application ofthe present invention in which the process, methods, algorithms andformulations used to investigate a number of news feeds and/or newscontents automatically and present the result to a client. In thisexemplary but important application system, the news are being firstcategorized automatically through finding the significanthead-categories and consequently clustering and bunching the news intoor under such significant head-categories and then select one or morepartitions of such cluster to represent the content of that clusterednews to a reader. Head-categories can simply being identified, byevaluating at least one of the significance measures introduced in thepresent invention, from those OSs that have exhibited a predeterminedlevel of significance. The predetermined level of significance can beset dynamically depends on the compositions of the input news.

It is important to notice that some of data in respect to any of thesefeatures (e.g. association of OSs) can be obtain from one composition(e.g. a good size of body knowledge) in order to be used ininvestigation of other compositions. For instance it is possible tocalculate the universal association of the concepts by investigation thewhole contents of Wikipedia (using, for instance, exemplary teachings ofpresent invention) and use these data/knowledge about the association ofconcept in calculating a relatedness of OSs of another composition (e.g.a single or multiple documents, or a piece or a bunch of news etc.) toeach other or to a query.

Moreover other complimentary representations, such as a navigableontological subject map/s, can accurately being built and accompany therepresented news. Various display method can be used to show thehead-categories and their selected representative piece of news or partof the piece of the news so that make it easy to navigate and get themost important and valuable news content for the desired category.Moreover the categorization can be done in more than one steps whereinthere could be a predetermined or automatic selection of majorcategories and then under each major category there could be one or moresubcategories so that the news are highly relevant to the head categoryor the sub-categories or topics.

Furthermore many more forms of services can be performed automaticallyfor this exemplary, but important, application such as identifying themost novel piece of the news or the most novel part of the news relatedto a head category or, as we labeled in this disclosure, to a target OS.Such services can periodically being updated to show the most updatedsignificant and/or novel news content along with their automaticcategorization label and/or navigation tools etc.

Referring to FIG. 12 now, it shows one general embodiment of a systemimplementing the process, methods and algorithms of the presentinvention to provide one or more services or output to the clients. Thisfigure further illustrates the method that a particular output orservice can in practice being implemented. The provider of the serviceor the outputs can basically utilizes various measures to select from oruse the various measures to synthesize the desired sought after part/sof an input compositions. A feature to be noticed in this embodiment isthat the system not only might accept an input composition forinvestigation but also have access to banks of BOKs if the service callsfor additional resources related to the input composition or as resultof input composition investigation and the mode of the service. Moreoveras shown the exemplary embodiment of system of FIG. 12 has a BOKassembler that is able to assemble a BOK from various sources, such asinternet or other repositories, in response to an input request andperforms the methods of the present invention to provide an appropriateservice or output data or content to one or more client. The filtrationcan be done is several parallel or tandem stages and the output could beprovided after any number the step/s of filtrations. The filters F₁, F₂,. . . F_(n) can be one of the significance measures or any combinationsof them so as to capture the sought after knowledge, information, data,partitions from the compositions. The output and the choice of thefilter can be identified by the client or user as an option besideseveral defaults modes of the services of the system.

Another block in the FIG. 12 to mention is the post-processing blockthat in fact has the responsibility to transform the output of thefilter/s into a predetermined format, or transform the outputsemantically, or basically composing a new composition as a presentableresponse to a client from the output/s of the filters of the FIG. 12.Also shown in this exemplary embodiment there is a representation modeselection that based on the selected service the output is tailored forthat service and the client in terms of, for instance, transmissionmode, web-interfacing style, frontend engineering and designs, etc.

Furthermore the exemplary system embodiment of FIG. 12 shows a networkbus that facilitate the data exchange between the various parts of thesystem such as the BOK bank (e.g. containing file servers) and/or otherstorages (e.g. storages of Los₁, Los₂, Los₃, etc. and/or liststorage/data wherein Los stands for List of the Ontological Subjectsand, for instance, Los₁ refers to the list of the OSs of order l) and/orthe processing engine/s and/or application servers and/or the connectionto internet and/or connection to other networks.

FIG. 13 shows another general embodiment block diagram of a systemproviding at least one service to a client. In this figure there is acomposition investigator wherein the investigator has access to a bankof bodies of knowledge or has access to one or modulus that can assemblea body of knowledge for client. Such said module can for example usesearch engines to assemble their BOK or from another repository ordatabase. The system can also provide one or more of the services of theFIG. 10 to a client. For instance the system is connected to the clientthrough communication means such as private or public data networks,wireless connection, internet and the like and either can receive acomposition from the client or the system can assemble a composition ora body of knowledge for the client and/or the system can enrich or addmaterials to the client's input composition and perform theinvestigation and provide the result to the client. For example, byinvestigating the input composition from the client or user, the systemcan automatically identifies the related subject matters to the inputcomposition and go on to assemble one or more BOK related to at leastone of the dominant OSs of the input composition and offer furtherservices or output such as the information regarding the degree ofnovelty of the input composition in comparison to one or more of saidBOK/s and/or score the input composition in terms of credibility oroverall score of the merits of the input compositions in comparison tothe said BOK/s and/or identify the substantially valuable and/or noveltyvaluable part or partitions of the input composition back to the user orother clients or agents. In light of the disclosed algorithms andmethod/s of the composition investigation there could be provided asoftware/hardware module for composition comparisons that provide one ormore of the services or the output data of the just exemplifiedapplication.

The mentioned exemplary application and service can, for instance, be ofimmense value to the content creators, genetic scientists, or editorsand referees of scientific journals or in principal to anypublishing/broadcasting shops such as printed or online publishingwebsites, online journals, online content sharing and the like.

Such a system can further provide, for instance, a web interface withrequired facilities for client's interaction/s with the system so as tosend and receive the desired data and to select one or more desiredservices from the system.

For instance it can be used as a system of interactive and socialknowledge discovery as introduced in the U.S. patent Ser. No. 12/955,496now the U.S. Pat. No. 8,775,365 entitled “Interactive and SocialKnowledge discovery Sessions” which was incorporated entirely as areference in this application.

Also as shown in the FIG. 13, other optional modulus can be madeavailable to the client that uses the main composition investigator andor the BOK assembler or BOK banks. A client can, for examples, be amachine, human, another software agent, an intelligent being, a remoteserver, or the like. One of such optional modulus can be a module forclient and computer or the client and system converse or conversation.The conversations is done in such a way that the system of thisexemplary embodiment with the “converse module” receives an input from aclient and identifies the main subject/s of the input and provide arelated answer with the highest merit selected from its own bank ofBOK/s or a particular BOK or an available composition. The response fromthe system to the client can be tuned in such a way to always provide arelated content according to a predetermined particular aspect of theconversation. For example, the client might choose to receive only thecontent with highest novelty yet credibility value from the system. Inthis case the “converse module” and/or the investigator module will findthe corresponding piece of content (employing one or more of the “XYvalue significant measure”) from their repositories and provided to theuser. Alternatively, for instance, the user can demand to receive themost significant yet credible piece of knowledge or content related toher/his/it's input. The client/system conversation, hence, can becontinued. Such conversation method can be useful and instrumental forvariety of reasons/applications such as entertainment, amusement,educational purpose, questions and answering, knowledge seeking,customer relationship management and help desk, automatic examination,artificial intelligence, and very many other purposes.

The system, for instance can be used as a system of providing orgenerating visual and/or multimedia content as introduced the U.S.patent application Ser. No. 12/908,856 entitled “System And Method OfContent Generation”, filed on Oct. 20, 2010, and or using the valuesignificance measures and the maps and indexes to automatically generatecontent compositions as introduced in the U.S. patent application Ser.No. 12/946,838, filed on Nov. 15, 2010, now U.S. Pat. No. 8,560,599 B2entitled: “Automatic Content Composition Generation”, which wereincorporated entirely as references in this application

In light of the teaching of this disclosure, such exemplified modulesand services can readily be implemented by those skilled in the art by,for instance, employing or synthesizing one or more the valuesignificance measures, and the disclosed methods of investigation,filtration, and modification of composition or bodies of knowledge.

FIG. 14, further exemplifies and illustrates an embodiment of a systemof composition investigation that one or more client are connected tothe system directly and one or more clients can optionally be connectedto the system through other means of communications such as private orpublic data network such as wireless networks or internet. In thisinstance the whole system can be a private system providing suchservices to its user or the system is composed of several hardware andnecessary software modules over a private network wherein the users canuse the services of composition investigation by the system directly orover the network. Such a system can in one configuration beingcharacterized as a private cloud computing facilities capable ofinteracting with clients and running the one or more of the process andalgorithms and/or implement and execute one or more of the relationalvalue significance calculations processes or implementation of one ormore of the formulas or equivalent process in their software module/s toprovide data/content and/or a desirable service of compositioninvestigation to one or more client.

FIG. 15, shows another exemplary instance of ubiquities system andservice provider in which the system can/might be a distributed systemand is using resources from different locations in order to perform andprovide one or more of the services. One or more of the functionperforms as shown in FIG. 15, might be physically located across adistributed network. For instance one or more of the calculations, orone or more of the servers, the front end server, or the client'scomputer or device can be located in different places and still theservices is performed over a distributed network. In this configurationan ISP who is facilitating the connection for a client to such adistributed network is regarded as the service provider of such service.Therefore a facilitator that facilitated (e.g. through a switch, routeror a gateway etc.) at least some of the request or response data eitherfrom the client or from any part of such a distributed service isregarded as instance of such a service provider system.

The data/information processing or the computing system that is used toimplement the method/s, system/s, and teachings of the present inventioncomprises storage devices with more than 1 (one) Giga Byte of RAMcapacity and one or more processing device or units i.e. data processingor computing devices, e.g. the silicon based microprocessor, quantumcomputers etc.) that can operate with clock speeds of higher than 1(one) Giga Hertz or with compound processing speeds of equivalent of onethousand million or larger than one thousand million instructions persecond (e.g. an Intel Pentium 3, Dual core, i3, i7 series, and Xeonseries processors or equivalents or similar from other vendors, orequivalent processing power from other processing devices such asquantum computers utilizing quantum computing devices and units) areused to perform and execute the method once they have been programmed bycomputer readable instruction/codes/languages or signals and instructedby the executable instructions. Additionally, for instance according toanother embodiment of the invention, the computing or executing systemincludes or has processing device/s such as graphical processing unitsfor visual computations that are for instance, capable of rendering anddemonstrating the graphs/maps of the present invention on a display(e.g. LED displays and TV, projectors, LCD, touch screen mobile andtablets displays, laser projectors, gesture detecting monitors/displays,3D hologram, and the like from various vendors, such as Apple, Samsung,Sony, or the like etc.) with good quality (e.g. using a NVidia graphicalprocessing units).

Also the methods, teachings and the application programs of the presentsinvention can be implement by shared resources such as virtualizedmachines and servers (e.g. VMware virtual machines, Amazon ElasticBeanstalk, e.g. Amazon EC2 and storages, e.g. Amazon S3, and the likeetc. Alternatively specialized processing and storage units (e.g.Application Specific Integrated Circuits ASICs, system/s on a chip,field programmable gate arrays (FPGAs) and the like) can be made andused in the computing system to enhance the performance and the speedand security of the computing system of performing the methods andapplication of the present invention.

Moreover several of such computing systems can be run under a cluster,network, cloud, mesh or grid configuration connected to each other by,data bus/es, communication ports and data transfers apparatuses such asswitches, data servers, load balancers, gateways, modems, internetports, databases servers, graphical processing units, storage areanetworks (SANs) and the like etc. The data communication network toimplement the system and method of the present invention carries,transmit, receive, or transport data at the rate of 10 million bits orlarger than 10 million bits per second;”

“Furthermore the terms “storage device, “storage”, “memory”, and“computer-readable storage medium/media” refers to all types ofno-transitory computer readable media such as magnetic cassettes, flashmemories cards, digital video discs, random access memories (RAMSs),Bernoulli cartridges, optical memories, read only memories (ROMs), Solidstate discs, and the like, with the sole exception being a transitorypropagating signal.

These applications and systems are presented to exemplify the way thatthe present invention method of investigation might be employed toperform one or more of the desired processes to get the respectiveoutput or the content, answer, data, graphs, analysis, and service/setc. Several modes of services and further applications are exemplifiedherebelow.

-   -   The processes and systems of FIGS. 8-15 can be an on premises        system, an intelligent being, or a network system of computation        and processing, storage medium, displays and interfaces, and the        associated software.    -   In another instance the systems and processes of the FIGS. 8-15        can be a remote system providing the service in the form of        cloud environment for one or more clients providing one or more        the services mentioned above.    -   Yet in another instance the system can be a combination of an on        premises private cloud/machine computation facilities connected        to a public cloud service provider. These familiar mode of        operation characterized as public and/or private and/or hybrid        cloud computing environment (either distributed or central, on        premises or remote, private or public or hybrid) is known to the        skilled to art and the disclosed methods of investigations of        compositions of ontological subjects can be performed in variety        of topologies which is regarded as service provider system        employing one or more of the generating methods/s of output data        respective of one or more of the disclosed methods of the        investigation of a composition of ontological subjects.    -   An interesting mode of service is when for an input composition        and after investigation the system yet provides further related        compositions or bodies of knowledge to be looked at or being        investigated further in relation to the one or more aspect of        the input composition investigation. Another service mode is        that the system provides various investigation diagnostic        services for the input composition from user.    -   Another mode of use is when an intelligent being make connection        or communicate with the system of composition investigation        (i.e. the brain) by way of communication networks to provide        desired services (e.g. conversing, telling stories, talking,        instructing, providing consultancy, generating various content,        manufacturing, etc.). In another instance the currently        disclosed method/s and system/s is implemented within the        intelligent being or used to realize new intelligent beings.    -   Furthermore the method and the associated system can be used as        a platform so that the user can use the core algorithms of the        composition investigation to build other applications that need        or use the service of such investigation. For instance a client        might want to have her/her website being investigated to find        out the important aspects of the feedback given by their own        users, visitors or clients.    -   In another application one can use the service to improve or        create content after a through investigation of literature.    -   In another instance the methods and systems of the present        invention can be employed to provide a human computer        conversation and/or computer/computer conversation such as        chat-bots, automatic customer care, question answering,        fortunetelling, consulting or any general any type of kind of        conversation.    -   In another mode a user might want to use the service of the such        system and platform to compare and investigate her/his created        content to find out the most closely related content available        in one or more of such content repositories (e.g. a private or        public, or subscribed library or knowledge database etc.) or to        find out the score of her/his creation in comparison to the        other similar or related content. Or to find out the valuable        parts of her/his creation, or find a novel part etc.        As seen there could be envisioned numerous instance of use,        products, beings, and applications of such process and methods        of investigating that can be implemented and utilized by those        of skilled in the art without departing from the scope and sprit        of the present invention.

II-VIII—Artificial Intelligent Systems Using Neural Networks

As disclosed in the U.S. patent application Ser. No. 14/607,588, filedon Jan. 28, 215, entitled “Association strengths and value significancesof ontological subjects of networks and compositions” a network ofobjects is considered a composition and vice versa. Accordingly themethods of investigation disclosed here are applied to build newapplications, services and products. Accordingly a network ofontological subjects can be a representative for a composition and viceversa. In particular artificial neural networks are therefore a form ora representative of a composition of ontological subjects itself whoseassociations of its ontological subjects (e.g. connections between nodesof the network) are to be known.

The popularity of the neural networks and the so-called deep learning isdue to its potential ability to train a network of connecting nodes tobecome able to map a certain set of data (e.g an input dada) to adesired set of data (e.g. the output data).

Currently the connection weight between nodes of a neural network isobtained by various training algorithms and processing which aregenerally rooted in stochastic gradient decent type of algorithms.

These methods are prone and notorious for non-converging ( . . . whichresult in relinquishing many of the useful parts of the neural netconcept in general) or being non reliable for critical tasks (they canbe fooled by slight noise introduction in the input data of such).

In training of such system having a good initializing of the state/s ofsuch network (e.g the initial weight or weight function betweenconnecting nodes) is of vital importance for the success of thetraining, ability and overall performance of the trained neural networksystem.

Referring to FIG. 11-A now, here we like to use the disclosed methodsfor building and training an artificial neural network for various eapplications such as categorizations, recognition, content generation orutterance generation. In here we show an exemplary multilayer neuralnetwork comprises of a number of neurons in each layer. The wholenetwork can have very many layers (e.g. hidden layers to provide extradegrees of freedom for optimization) each node can be considered orassigned with an ontological subjects of predefined order (e.g. such aseach node in the first layer can be represented of a textual word) thesecond layer.

Each node (e.g. a neuron or perceptron) in each layer is connected to anumber of other nodes in its preceding layer and to a number of nodes onits consequent layer. The role of neural network is to learn the impactof each input/neuron to other neuron in other layers either directly orindirectly (through hidden layers).

The fundamentals of neural networks and more recently deep learningneural networks are straightforward and is known in the literature.Basically the aim of learning/or training of a neural network is to findor adjust the weight/impact of each node to/from its connecting nodes.

The training of any reasonably useful neural network however is not atrivial undertaking needing a large number of highly specializedprocessing devices (e.g expensive Graphical Processing Units) and a longtraining time.

In FIG. 11-A, it can be shown that a matrix of N×M will map the N inputsof the network in FIG. 11-A to the desired number of outputs (e.g M).

Lets call such a matrix A which would be a N×M matrix and itself can bedecomposed to number of ( . . . in fact it can be decomposed to infinitenumber of other matrixes) like the followings:

Matrix  A  with  dimetion  of  N × M = A 1(dimention:  N × M 1) × A 2(dimentions:  M 2 × M 3) × …  An(dimentions:  Mn × M)

wherein A1, A2, . . . An are matrixes with dimensions specified in theabove equations. Each intermediate matrix can be corresponded to theconnections of nodes of adjacent layers. These intermediate matrixesshow the connection and the weight of the connections between nodes ofadjacent layer or back propagating connections from other layers.Computationally and in practice training of a neural networkstarts/initialized with a randomly populated matrixes and the values arechanges and varied through various computational algorithms until thedesired results are achieved satisfactorily. Such desired results fromthe network could be that the network become able to classify an imagecorrectly with high degree of probability, or distinguishes an audiosignal and extract or convert the audio signal to its corresponded orequivalent text, and/or translating text/voice between languages etc.

Regardless of the application of a neural network, however, each ofthese intermediate, matrices that will collectively make the wholeneural network to perform a task, are to be fund which is the goal ofneural networks learning algorithms. It is conceptually easy to see thatif a node (i.e. a neuron) is connected to/from another node so theywould have some sort of relationships and or, using the terms of thisdisclosure, some types of associations and relationship with each other.

Accordingly it is easy to see that the goal of neural of networktraining algorithms is in fact trying to find a degree or a forceintensity or influence or in other word the strength of the associationsbetween the nodes that make up the neural network.

Now considers that nodes of the first layer are corresponded toOntological subjects of order i and the nodes of a second layer arecorresponded or representatives of Ontological subjects of order j (iand j can be the same or equal) and . . . .

For instance, in an exemplary embodiment shown in FIG. 11-A, nodes ofthe first layer can be regarded or been representative of textual wordsof a natural language such as words of English languages as input to asystem of networks of nodes (e.g. Neural Networks, the so called deeplearning neural nets, or any other network of objects with some dataprocessing function). The nodes in the second or third layer can berepresentatives of sentences or English words again (i=j) whereas thenodes of third layer can be representative of word phrases, sentences,paragraphs, textual templates (sentence template, paragraph templatescontaining one or more words), and so on. Same can be said for otherlayers between the input and output layer. (Same can be done for varioussets of partitions of images and pictures as will be discussed orespecifically in the next section).

Currently to find such relationship between theses nodes the neural netneeds to be trained with huge number of data sets and corpuses in orderto have relatively a meaningful working neural network and sensibleoutput.

Without going into the details of shortcoming of such training anddrawbacks of neural network to perform intelligent tasks, here it isaimed to use the data objects (e.g. various association strengthmatrices, various significance values etc.) of this disclosure which areobtained or built by exercising the teachings of this disclosure tobuild a neural networks both in hardware or software shape with theinitial connections and weights are obtained by calculating for exampleASM of different types and order and if it is needed further train theneural network to even function better. Said neural network further canbe implemented as various classes/types of recurrent neural networks,convolutional neural networks, recursive neural networks, neural historycompressor, feed forward neural networks and the like.

The advantage of using ASM/s to build a neural network is threefold asoutlined next,

-   -   1. First: using the data of ASM/s we would know which nodes has        to be connected to each other rather than blindly connecting        every node to every other node. Currently to get a satisfactory        result one have to have very large number of neurons at each        layer (in order of millions to billions) and connecting the        nodes to each other as much as possible in order to have enough        parameters to play with to eventually synthesize an unknown        function (e.g. the artificial intelligent brain).        -   Using the data of associations from this disclosure            therefore can reduce the size of the neural network            significantly.    -   2. Secondly, since the data (e.g the entries of ASM matrix or        connection weight between the nodes) are close to their actual        values in really world, further adjustments to improve the        performance of the artificial neural network would converge much        quicker while the performance of the whole network (as an        artificial brain) would be significantly enhances.    -   3. Thirdly, Since we have introduced various data objects and        various types of associations and relationships between the        ontological subjects of a composition or very large set of        compositions the neural network become programmable and        therefore the designer of such systems has control and insight        into to working mechanics of the artificial intelligent system        (e.g. a robot or self-driving car/robot etc) which employs an        artificial network of ontological subjects (e.g neural network).        In this way the designer of such system have advance knowledge        and expectation form the system whereas currently the neural        networks are trained by brute forces and sheer processing power        of processing devices such as NVidia graphical processing        accelerators.

To summarize this section the disclosure introduces an artificialintelligent system which uses the various data objects of from theinvestigator of FIG. 10 to build and train further a network ofontological subjects (a neural net is an instance of network ofontological subjects) to perform intelligent tasks and to implementmachine learning by investigating one or more bodies of knowledge tolearn about the world.

There could be two different systems to build the AI system here. Onneis that the investigator is part of the system and second is that the AIsystem (e.g. The hardware or software system) uses the data objects ofthe investigator in order to learn and train itself much faster, usingminimal number nodes as necessary and much efficient while become muchmore affordable.

Such a system then is incorporated into mechanical systems such asspecial purpose or general purpose robots and intelligent systems andmachines.

II-IX—Investigation Visual Compositions and Image Processing

In this section another instantiation, application and system of imageprocessing is presented. The system of image processing is basically thesystem of FIG. 10, wherein, as shown in FIG. 11-B, exemplaryillustration is given as how to apply the methods of this disclosure toprocess image data and gain them.

After the processing of the image/s, the system of image processing canclassifies related or similar images, through calculating variousAssociation and Significance values of Ontological Subjects of visualnature and order.

As seen in FIG. 11-B, one can initially partitions an image or a movieframe down to its individual RGB components of its pixels as Ontologicalsubjects of order zero, then regards a pixel as composition of RGBs ormore conveniently as OSs of order l, then, for example, every twoadjacent pixels (horizontally and/or vertically as desired) as OSs of 2,and every 2 of OSs of order 2 as OSs of order 3 and so on. In thisparticular illustrations, for example, an OSs of order k is in factcomposed of 2^(2(k−2)) (for k>2) pixels. Obviously one may elect topartition the image in another fashion and user different order for anycertain number of pixels.

In this way we become able to transform the information of a pictureinto existence of such ordered ontological subjects into each otherthrough constructing data objects or one or more data structurescorresponding to the participation matric/es of various order asdescribed and defined several times along this disclosure and/or theincorporated references herein.

Further the lists of OSs of particular order defined for visual objectscan be a set (all identical OSs represented with one of such) or belisted as they appear in the picture.

Setting the ordered ontological subjects of the picture will make thePMs less data intensive resulting faster processing and shortening theimage processing task thereof. Furthermore sometimes said setting canalso enhance the functionality of the process and lessen the clutters.For instance, if the desired function of the process is to categorizethe visual objects, setting the OSs may help to reduce unnecessary noisebeside the data processing effect.

For some other applications however, it might be desirable to keep allthe OSs of any order as they appeared in the picture. In this case indexof that OSs in a PM also bears the geometrical information of that OSs(partitions of the picture) in the picture.

For instance the index of the ontological subjects (the index of thecolumn or the rows that each OS will be represented in the participationmatrix) bears a very important information about a picture and can beused geometrically to characterize a picture. For instance the ratio ofthe j index of significant OSs of order 3 of the picture can be used asfurther information to characterize the picture. New data objects andMatrix/es can be constructed to convey the information of some of theselected OSs of certain order of the image frame/picture respect to eachother. Furthermore gain, such geometrical information and/or their ratiocan be normalized so that they can be used for comparing to otherprocessing needs (correlating a picture in a standard way to a group ofother pictures).

Again, the data objects of the present invention (e.g. varicose PMs,ASMs, VSMs, vectors or matrices) can be adequately described as being arepresentation of points in a Hilbert space and linear transformationsof the data objects does not have drastic effect on the quality andcontinuity of the investigation results. Most other transformation (suchas rotating an image, i.e. rotating the data of its correspondingparticipation matrix, or other mathematical operations on the dataobjects) also would not cause a discontinuity type of effect on thebehavior of the result of desired data, e.g the result of a noveltydetection or finding significant partitions/segments or edge detectionetc, of an image. In other words the disclosed image processing methodis much more robust and process efficient than the image processing withneural networks, or deep learning, convolutions neural nets, andclassical image processing methods.

Nevertheless as is the case with the textual compositions, the result ofinvestigation of visual compositions, e.g. the presented imageprocessing, can be used to build a more efficient and compact neuralnetworks than building a heuristically large neural network. Moreoverthe data objects that are generated after investigations of a bodyknowledge, composed of a number of images, can be used to initialize theneural networks for further training. Since the data of theinvestigation results (e.g. ASMs, VSMs, RASMs and other data objects ofthis disclosure) like) are obtained from existing and real images (or ingeneral exhibiting ontological subjects rather than randomly possiblyexisting Ontological subjects) a deep learning network built andinitialized (by using the data of the presented investigation method ofcompositions of ontological subjects) is more likely to converge, andconverge faster.

The process is efficient in doing intelligent actions and decisionmaking based on a received or input image/picture. Another advantage ofusing the present invention as a method of image processing inapplication ranging from computer vision, navigation, categorization,content generation, gaming and many more, is that the method/s is lesssensitive to the orientation and angle and almost invariant since manydata objects are built during the investigation that are assigned tosegments of deferent sizes of the image. Accordingly using or more ofthese data objects or a combination of different ASM/VSM measures andthe information that are extracted from the images during theinvestigation process, one can assign a distinguishable signature to aninput images.

Once the image is partitioned into segments of predefined sizes orpluralities of ontological subjects of different orders calculating thenobtaining data objects of interests become similar to the described indetailed methods for the textual compositions (see Eq. 1-64).

Accordingly the system of image processing based on the teaching of thisdisclosure become able to provide all functionalities of FIG. 10. Anexemplary application therefore would be in computer vision forclustering or classification of images characterization of images, andthen acting upon such characterization and recognition.

In particular, for robot visions, autonomous robots, intelligent expert(e.g. medical assistant robots), autonomous or semi-autonomoustransportation robots (e.g. self-driving car, truck, drone, self-flyingobjects, etc.).

Once an image is characterized and its relation to a cluster, categoryor class become known (e.g. see the incorporated co-pending U.S. patentapplication Ser. No. 15/589,914) a system/machine, comprising the imagesprocessing/investigation of the present disclosure, can issue furtherinstructions or signals to be used by other systems or parts (e.g.another the machine, software, robot, intelligent being etc). Suchsystems/machines can therefore achieve a cognition and understanding oftheir surrounding environment. Further using the present disclosuremethod of investigation of compositions such systems and machines arecapable of conversing and exchanging data and knowledge not only withother machines but also with human by conversing with human clientsthrough human consumable languages or content such as voice or machinegenerated multimedia content.

For instance using the Novel relational associations measure (E.q. 1-38and 39 onwards) the investigator system of FIG. 10 become able todistinguish movements and their speed (as shown in FIG. 11-B visual OS scan be traced by their indices in the partitioned images and thereforepartitions of the consequent images of live a camera (i.e. movie frames)can be traced and their identity and motion can be calculated by usingtheir indices in the partitioned image.

One particular use of the methods and algorithm of this disclosure inthis would be ranking of the images based on relational valuesignificances using association strengths values of Ontological Subjectsof different order.

An interesting system is for image recognition when ranking an inputimage as how that could be related to an ontological subjects (forexample how an image is close or contain certain object or living thingetc.) for instance whether there is tree in the image.

In such system for this application the system of FIG. 10 comprisingdata processing or graphical processing units have the details of a treepicture along with partition as number of sets of ontological subjectsof predefined order as been illustrated in FIG. 11-b.

Then among a body of compositions of images we can identify whether aninput images contain certain ontological subject (considering that onecan regard a whole image of tree/tress as OS of order 4, 5, or higher)then its constituent partitions such single pixels as OS order 1, 2pixel partitions as set of OSs of order 2, 4 pixel partitions as set ofOSs of order 3, 16 pixels partitions as set of OSs of order 4 and so on.

One can find the associations of the partitions of the picture and usingsome or all the Eqs. 1-64 to build data structures, programming a GPU,program an FPGA, design a system on chip, design and build anapplication specific computing devices such as ASIC using silicon orIII-V materials, a data processing apparatus comprising one or morecomputing or data processing devices, and to evaluate or score or rankthe relevancy of an input image/picture to a target or desiredimage/picture, category, concept, function, signal, or instructing amachine or order a machine to perform a desired task or operations.

For example how closely an input image or picture is related to certainentity/ies, like a Cat, a Tree, a House, A car, A passenger, a movableobjects (as the target Ontological subject). Or when there are verynumber of images then use the method for classification andcategorization of images.

Of course the image/pictures can be preprocessed by known digital signalprocessing to do for example, rotate the input picture once or more withcertain angle, change the orientation, resize the image/picture to apredefined pixel size, or a desired height and width, or predefineddimension (e.g every picture transformed re scaled, or resizes to320*320 pixels or to a 1000 by 1000 pixels, or one Mega pixels etc.)Further the range of possible combinations (R, G, B), with or withoutthe pixel depth data, can be changed or reduced. For example theimage/picture can be transformed to gray scale only, or range of pixelcolor be reduced to a desired number of colors, e,g. from 256×256 x256number of colors be reduced to 16×16×16 number of colors or the like.

Using the novel type of association or novel relational association, acomputer vision system is built using the one or more of theinvestigation methods of this disclosure or using the data objects ofthe investigator to interpret and track the novelty to theircorresponding ontological subjects (e.g. a cat is moving near a tree) inorder to build a computer vision system to be used in systems requiringvision cognitions (e.g. using in humanoid Robots and/or self-derivingcar/robots or drowns security systems etc.)

In practice, the data volume an image frame or an image file is way morethan the data of an average text file. Accordingly the processing timeof an image frame especially if it is a high definition image, isconsiderably higher. Also consider that usually the image in somescenarios or embodiments is processed with a large number of otherpictures of the same category or a diverse group or number of images.

Therefore, in one exemplary method, application, and system of imageprocessing with teachings of this disclosure we use graphic processingunits, each having one or more processing cores, coupled with enoughrandom access computer readable memories (e.g. RAMs) to accelerate thecomputing speed.

One or more graphic processing units are programmed to receive an imageframe, for instance from a video port, process the image encoded imagedata to partition the image and extract the constituent Ontologicalsubjects of different order, build the participation matrix/es, buildone or more Association strength measure between ontological subjects ofthe said image. The ASM could be calculated for Ontological subjects ofthe same order or different order, each order corresponds to partitionor a segments of various size of the image (as described before).Further building data structures corresponding to value significance ofthe portions of at least one order. Further calculate other data objectsof various type such RASM, RNASM, VSMs, NVSMs, and any other desireddata objects expressed by Eq. 1-65 to investigate the image or group ofimages as outlined in FIG. 10 for example. And further execute theinstructions by the processing units to do at least one of the exemplaryapplications disclosed in this disclosure (such as clustering a largenumber of images into one or more categories, novelty detection,summarization, recognition, tagging, transforming to text,reconstruction of an image with certain desired features, constructionof other images, new image creation etc.) or further process the imageto do other desirable functions based on the data of the investigationresults. The processing units further or coupled with other processingdevices can control other machines, artificial limbs, robots or decideon further actions and/or executing other functions and processing.

II-X—Summary

The disclosed frame work along with the algorithms and methods enablesthe people in various disciplines, such as artificial intelligence,robotics, information retrieval, search engines, knowledge discovery,genomics and computational genomics, signal and image processing,information and data processing, encryption and compression, businessintelligence, decision support systems, financial analysis, marketanalysis, public relation analysis, and generally any field of scienceand technology to use the disclosed method/s of the investigation of thecompositions of ontological subjects and the bodies of knowledge toarrive the desired form of information and knowledge desired with ease,efficiency, and accuracy. Since the disclosed underlying theory, methodsand applications are universal it is worth to implement in the system ofexecuting the methods and products directly on processing chips/devicesto further increase the speed and reduce the cost of such investigationsof compositions. In one instance, for example, the data processingoperations (e.g. vector/matrix manipulations, manipulating datastructures, association spectrums calculations and manipulation, etc.)and even storage of the data structures, is implemented with designs ofApplication Specific Integrated Circuits (ASICS), or Field-ProgrammableGate Arrays, (FPGA), or the system-on-chip, based on any computing anddata processing device manufacturing platforms and technologies, such assilicon based, III-IV semiconductors, and quantum computing artifacts toname a few. Similarly, if the disclosed methods of the investigation andapplications are going to be used in/with implementing neural orcognitive based type of computations, still one can implement the systemon such chips and by said technologies. Accordingly, those competent inthe art can implement the disclosed methods for variousapplications/products in/with various data processing devicemanufacturing and designs on the physical material level.

The invention provides a unified and integrated method and systems forinvestigation of compositions of ontological subjects. The method can beimplemented language independent and grammar free. The method is notbased on the semantic and syntactic roles of symbols, words, or ingeneral the syntactic role of the ontological subjects of thecomposition. This will make the method very process efficient,applicable to all types of compositions and languages, and veryeffective in finding valuable pieces of knowledge embodied in thecompositions. Several valuable applications and services also wereexemplified to demonstrate the possible implementation and the possibleapplications and services. These exemplified applications and serviceswere given for illustration and exemplifications only and should not beconstrued as limiting application. The invention has broad implicationand application in many disciplines that were not mentioned orexemplified herein but in light of the present invention's concepts,algorithms, methods and teaching, they becomes apparent applicationswith their corresponding systems to those familiar with the art.

Among the many implications and application, the system and method havenumerous applications in knowledge discovery, knowledge visualization,content creation, signal, image, and video processing, genomics andcomputational genomics and gene discovery, finding the best piece ofknowledge, related to a request for knowledge, from one or morecompositions, artificial intelligence, realization of artificially ornew intelligent begins, computer vision, computer or man/machineconversation, approximate reasoning, as well as many other fields ofscience and generally ontological subject processing. The invention canserve knowledge seekers, knowledge creators, inventors, discoverer, aswell as general public to investigate and obtain highly valuableknowledge and contents related to their subjects of interests. Themethod and system, thereby, is instrumental in increasing the speed andefficiency of knowledge retrieval, discovery, creation, learning,problem solving, and accelerating the rate of knowledge discovery toname a few.

It is understood that the preferred or exemplary embodiments, theapplications, and examples described herein are given to illustrate theprinciples of the invention and should not be construed as limiting itsscope. Those familiar with the art can yet envision, alter, and use themethods and systems of this invention in various situations and for manyother applications. Various modifications to the specific embodimentscould be introduced by those skilled in the art without departing fromthe scope and spirit of the invention as set forth in the followingclaims.

What is claimed is:
 1. A system for training a network of nodescomprising: one or more computing or data processing devices operativelycoupled to one or more computer readable non-transitory storage media,accessing, by the one or more processing devices, a body of knowledge ora composition of ontological subjects, partitioning the composition intoseveral pluralities of ontological subjects, wherein each plurality isassigned with a predefined order, building, by one or more of theprocessors, one or more data structure corresponding to participation ofsome of ontological subjects of the composition, having assigned with afirst predefined order, into some of ontological subjects of thecomposition having assigned with a second predefined order, storing saidone or more data structure into said one or more non-transitory computerreadable storage media, calculating, by processing said one or more datastructure by said one or more computing or data processing devices,value significances of at least some of the oncological subjects of thecomposition, having assigned with the first or the second predefinedorder, according at least one measure of significance, calculating, byprocessing said one or more data structure by one or more computing ordata processing units, association strength values between some of theontological subjects of the composition, according to at least onemeasure of association strength measure, building a network of nodescomprising several layers, each layer having one or more nodes,including input and an output layers, wherein some of nodes of eachlayer are connected to some nodes of another layer, and wherein some ofthe nodes are representative of some of the ontological subjects of thecomposition of a desired order, wherein the connection weight betweensaid nodes are derived from said value significances and saidassociation strength values of some of said ontological subjects of thecomposition; and wherein the weight can be further altered based on dataentered in the input layer.
 2. The system of claim 1, wherein saidnetwork of nodes comprises a convolutional neural network.
 3. The systemof claim 1, wherein number of said nodes in at least one of the layersis selected based on the value significances of some of the ontologicalsubjects of the body of knowledge.
 4. The system of claim 1, whereinnumber of said nodes in at least one of the layers is selected based onthe association strength values between of some of the ontologicalsubjects of the body of knowledge.
 5. The system of claim 1, whereinconnection weights between some of the nodes of the network areinitialized based on the association strength values between some of theontological subjects of the body of knowledge.
 6. The system of claim 1,further comprising clustering said body of knowledge into severalcategories by processing the data of association strengths and valuesignificances of the ontological subjects of the composition.
 7. Agraphical processing unit for processing one or more image compositioncomprising: one or more graphic processing units, each having one ormore number of cores, accessing data corresponding to said one or moreimage composition; partitioning an image from the one or more imagesinto two or more plurality of predefined number of pixels, generating,using one or more computing or data processing devices, a first one ormore data structure, corresponding to participation of some of partitionof said image with a first predefined order into other partitions ofsaid image having a second predefined order, storing said first one ormore data structure to at least one non-transitory computer readablestorage media, assigning and calculating a value significance for someof the partitions of the image, by processing said first one or moredata structure, according to least one measure of significance value,assigning and calculating association strengths between some of thepartitions of said image, by processing said first one or more datastructure, building a second one or more data structures correspondingto data of at least one said value significances or said associationstrength of ontological subjects of the body of knowledge, storing saidsecond one or more data structure in one or more non transitorycomputer-readable storage media.
 8. The graphical processing unit ofclaim 7, further comprising one or more computing or data processingdevices and executable instructions operable to cause the one or moregraphic processing units or the one or more computing or data processingdevices to: re-scale at least one of the images to a different cellwidth and cell height.
 9. The graphical processing unit of claim 7,further comprising executable instructions operable to cause the one ormore graphic processing units to cluster said set of one or more imagesinto at least one cluster by processing said first or second one or moredata structure and calculating association strengths of each of said setof one or more images to each other, based on at least one measure ofassociation strength.
 10. The graphical processing unit of claim 7,further comprising one or more computing or data processing devices andexecutable instructions operable to cause the one or more graphicprocessing units, or the one or more computing or data processingdevices, to evaluate or score or rank the relevancy of an input image toa desired target, wherein said desired target is one or more of: animage, a category, a concept, a function, or a signal.
 11. The graphicalprocessing unit of claim 10, further comprising executable instructionsoperable to cause the one or more graphic processing units or the one ormore computing or data processing devices, to instruct a machine toperform a task or operations based on said score of relevancy of theinput image to one of said desired targets.
 12. The graphical processingunit of claim 7, further comprising computer vision system andexecutable instructions operable to cause the one or more graphicprocessing units to calculate novel type of association or novelrelational association between the partitions of said body knowledgecomposed of a set of one or more images.
 13. The graphical processingunit of claim 7, wherein the images are partitioned into two or moreplurality of partitions assigned with predefined orders wherein eachpartition of each plurality of partitions, assigned with a predefinedorder k and k>=1, having 2^(k−1) number of pixels.
 14. A non-transitorycomputer readable medium having executable instructions operable tocause a data processing apparatus to process a body of knowledgecomposed of one or more images comprising: generating, from encodedimage data of said one or more images, several pluralities of imagepartitions, wherein each plurality of partitions having deferent pixelsizes, or pixel data part, and is assigned with a predefined ontologicalsubject order, generating, using one or more computing or dataprocessing devices, a first one or more data structure, corresponding toparticipation of some of partition of said image with a first predefinedorder into other partitions of said image having a second predefinedorder, storing said first one or more data structure to at least onenon-transitory computer readable storage media, assigning andcalculating a value significance for some of the partitions of theimage, by processing said first one or more data structure, according toleast one measure of significance value, assigning and calculatingassociation strengths between some of the partitions of said image, byprocessing said first one or more data structure, building a second oneor more data structures corresponding to data of at least one said valuesignificances or said association strength of ontological subjects ofthe body of knowledge, storing said second one or more data structure inone or more non transitory computer-readable storage media.
 15. Thenon-transitory computer readable medium of claim 14, further comprisingexecutable instructions operable to cause the data processing apparatusto: re-scale at least one of the images to a different cell width andcell height.
 16. The non-transitory computer readable medium of claim14, further comprising executable instructions operable to cause thedata processing apparatus to cluster said set of one or more images intoat least one cluster by processing said first or second one or more datstructure and calculating association strengths of each of said set ofone or more images to each other, based on at least one measure ofassociation strength.
 17. The non-transitory computer readable medium ofclaim 14, further comprising executable instructions operable to causethe data processing apparatus, comprising one or more computing or dataprocessing devices, to evaluate or score or rank the relevancy of aninput image to a desired target, wherein said desired target is one ormore of: an image, a category, a concept, a function, or a signal. 18.The non-transitory computer readable medium of claim 17, furthercomprising executable instructions operable to cause the data processingapparatus to instruct a machine to perform a task or operations based onsaid score of relevancy of the input image to one of said desiredtargets.
 19. The non-transitory computer readable medium of claim 14,further comprising computer vision system and executable instructionsoperable to cause the data processing apparatus to calculate novel typeof association or novel relational association between the partitions ofsaid body knowledge composed of a set of one or more images.
 20. Thenon-transitory computer readable medium of claim 14, wherein the imagesare partitioned into two or more plurality of partitions assigned withpredefined orders wherein each partition of each plurality ofpartitions, assigned with a predefined order k and k>=1, having 2^(k−1)number of pixels.